system

Abstract

We provide the system. [Solution] A means for acquiring audio data, A text conversion means for converting acquired audio data into text data, A translation method that translates converted text data into another specified language, A speech synthesis means for converting translated text data into audio data, A voice transmission means that provides the converted audio data to the user's hearing device, A system that includes this.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional voice translation systems rely on external terminals such as smartphones and tablets for voice input, and the problems are the labor of operation and the time delay until translation. For this reason, users were often hindered from having natural conversations. Also, it was difficult to have smooth real-time voice communication, especially preventing people speaking different languages from easily communicating with each other. The purpose of this invention is to technically solve these problems and provide a smoother and more hassle-free voice translation experience.

Means for Solving the Problems

[0005] This invention provides a system based on wireless earphones, combining voice acquisition means, text conversion means, translation means, speech synthesis means, and voice transmission means. The voice acquisition means acquires the user's speech in real time, and the text conversion means converts the voice data into text. Furthermore, the translation means translates this text into another language, and the speech synthesis means converts it back into speech. The converted speech is output directly to the wireless earphones through the voice transmission means, allowing the user to converse in a natural manner. This enables rapid translation without the need for an intermediate terminal and minimizes latency.

[0006] A "voice acquisition means" is a device equipped with the function of collecting voice from a user and converting it into a format that can be processed as digital data.

[0007] "Text conversion means" refers to a device or software function that performs the process of converting audio data into text data.

[0008] A "translation tool" is a component that has the function of converting text written in the original language into another specified language.

[0009] "Speech synthesis means" refers to a device or software that has the function of converting text data into speech data and outputting it in a format similar to human speech.

[0010] "Audio transmission means" refers to a device or function that transmits converted audio data to the user's hearing device and allows it to be played back in real time.

[0011] "Wireless earphones" are small audio devices that are worn in the user's ears, can connect to other devices using wireless communication technologies such as Bluetooth, and are capable of inputting and outputting audio data. [Brief explanation of the drawing]

[0012] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention relates to a real-time translation system combining wireless earphones and a server. This system uses a speech acquisition means to acquire the user's speech and a text conversion means to convert that speech into text. The converted text is then translated on the server side by a translation means into the language specified by the server.

[0034] The server converts the translated text into audio data using speech synthesis technology and transmits that audio to wireless earphones in real time. By listening to this audio through the earphones, the user can hear what they have said played back in another language, and also hear what others have said in a translated state.

[0035] As a concrete example, when a user speaks Japanese, the wireless earphones capture the speech and transmit it to a server. The server converts the received audio into text, translates it into, for example, English, and then uses speech synthesis to send the English audio data back to the wireless earphones. As a result, even if the user speaks in Japanese, the content will be automatically translated into English and conveyed to the person speaking another language. This makes it possible to provide real-time, seamless communication between different languages.

[0036] In addition, wireless earphones, as terminals, are lightweight and highly portable, eliminating the need for users to carry around bulky traditional translation devices. Furthermore, by utilizing high-performance AI on a server, more accurate translations can be performed, providing users with a more natural conversational experience. This system configuration significantly improves the practicality of real-time translation, and its use is expected to expand to a wide range of situations.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The user wears wireless earphones and speaks what they want to say in their own language. The earphone's microphone captures the audio and processes it to send it to the device as digital data.

[0040] Step 2:

[0041] The terminal receives the acquired voice data and transfers it to the server using wireless communication. During this process, the voice data may be compressed or encrypted to minimize latency.

[0042] Step 3:

[0043] The server analyzes the received audio data and converts it into text data using speech recognition technology. This converted text is then used as input data for subsequent translation processes.

[0044] Step 4:

[0045] The server translates text data into the specified target language. By using an AI-based translation engine, it performs context-aware, highly accurate translations.

[0046] Step 5:

[0047] The server converts the translated text into speech data using speech synthesis technology. This speech data is generated in a form that closely resembles natural speech and is in a format that users can easily understand.

[0048] Step 6:

[0049] The server sends audio data to the terminal. During transmission, the audio data is appropriately formatted to maintain sound quality and enable real-time playback.

[0050] Step 7:

[0051] The device receives audio data transmitted from the server and plays it back to the user's ears through the speaker in the wireless earphones. By listening to the translated audio, the user can understand the other person's language and communicate in real time.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] This real-time translation system aims to overcome challenges in facilitating communication between different languages, such as the lack of portability of conventional devices, limitations in translation accuracy, and the disruption of natural conversation due to delays in audio data.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for acquiring audio signals using an acoustic acquisition device and converting them into text data using a character conversion device, means for translating them into another specified language using a translation device, and means for converting them into audio data using an acoustic synthesis device. This makes it possible to provide real-time and natural communication between different languages ​​through a highly portable device.

[0057] An "acoustic acquisition device" is a device that acquires audio signals and converts them into a processable data format.

[0058] A "character conversion device" is a device that analyzes acquired audio signals and converts them into corresponding character data.

[0059] A "translation device" is a device used to convert text data into another specified language.

[0060] A "sound synthesis device" is a device that converts translated text data back into sound data and outputs it in a playable format.

[0061] A "voice transmission device" is a device that provides converted audio data to the user's hearing device.

[0062] "Wireless communication" is a technology that transmits data without using an energy medium, and generally refers to methods that use radio waves.

[0063] "Remote computing resources" refer to computing devices and processing power located in physically distant locations that are accessible via a network.

[0064] This invention is a system for real-time communication between different languages ​​and includes a series of devices for acquiring, translating, and reproducing audio signals. The user speaks using wireless earphones as an audio acquisition device. The earphones acquire the audio signal and transmit this data to a server via a terminal.

[0065] The server converts the received audio data into text data using Google® Cloud Speech-to-Text or an equivalent speech recognition API. Next, it translates this text data into the specified language using a generative AI model. OpenAI®'s GPT is one example of an AI model that can be used here. For translation, a prompt such as the following can be used: "Translate the following Japanese into English: 'It's a nice day today.'"

[0066] The translated text data is synthesized into audio data using Amazon Polly or the Google Text-to-Speech API. The server transmits this audio data to the user's wireless earphones with low latency, allowing the user to hear the translated audio. This enables users who speak different languages ​​to communicate naturally.

[0067] Through the process described above, this system provides highly portable, accurate, and real-time translation, enabling users to enjoy comfortable international communication. For example, a user's Japanese utterance, "Ohayou gozaimasu" (Good morning), is transmitted to the other party in real time via the server process as "Good morning" in English. This system supports efficient dialogue in a variety of international situations.

[0068] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0069] Step 1:

[0070] The user speaks through wireless earphones. This audio signal is directly captured by the earphone's microphone. The input is the user's spoken audio signal, and the output is digitized audio data. At this stage, the audio is converted from analog to digital and temporarily stored within the device.

[0071] Step 2:

[0072] The terminal transmits digitized audio data to the server via wireless communication technology (e.g., Bluetooth). The input is the digital audio data stored on the terminal, and the output is the audio data sent to the server. In this process, the data is divided into packets, error checked, and then forwarded to the server.

[0073] Step 3:

[0074] The server processes the received audio data and converts it into text data using a speech recognition API (e.g., acoustic recognition technology). The input is the digital audio data received by the server, and the output is the converted text data. At this stage, the audio is analyzed to identify the exact string from the phonemes in the speech.

[0075] Step 4:

[0076] The server uses a generative AI model to translate acquired text data into a specified language. The input is text data, and the output is translated text data. A prompt "Translate the following text" is sent to the generative AI model, and the translated text is obtained as a response.

[0077] Step 5:

[0078] The server converts the translated text data into speech data using a speech synthesis API (e.g., audio synthesis technology). The input is the translated text data, and the output is the synthesized speech data. At this stage, the speech waveform is synthesized and regenerated as natural speech.

[0079] Step 6:

[0080] The server transmits synthesized audio data to the terminal with low latency using wireless communication technology. The input is the synthesized audio data on the server, and the output is the received audio data. The data necessary for playback is transmitted to the terminal in real time.

[0081] Step 7:

[0082] The device outputs the received audio data to the user's wireless earphones. The input is the received audio data, and the output is the audio the user hears. The user can hear the translated audio in real time and communicate smoothly with others.

[0083] (Application Example 1)

[0084] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0085] There is a need to provide means for people who speak different languages ​​to communicate smoothly. However, existing translation devices are large and inconvenient to carry, making them difficult to use in daily life or while traveling. Furthermore, there is a lack of systems that can provide a smooth and natural conversation experience. To solve these problems, a new translation system is needed.

[0086] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0087] In this invention, the server includes an information acquisition means for acquiring audio data, a character conversion means for converting the acquired audio data into character data, and a translation device for converting the converted character data into another specified language. This enables smooth, real-time communication between people who speak different languages.

[0088] "Information acquisition means" refers to devices or processes that have the function of collecting audio or data.

[0089] A "text conversion means" refers to a device or process that has the function of converting acquired audio data into text information.

[0090] A "translation device" is a device or process that has the function of converting textual information from one language to another.

[0091] "Speech regeneration means" refers to a device or process that has the function of converting text information into speech data and outputting it.

[0092] "Voice supply means" refers to a device or process that has the function of providing converted voice data to a user.

[0093] Wireless communication is a technology that uses radio waves to transfer data without using cables.

[0094] A "processing device" refers to a computer system or server used for data processing.

[0095] "Aircraft" refers to the entire device that is equipped with voice acquisition means and is used to process or output information.

[0096] To implement this invention, it is first necessary to prepare a device for collecting voice as an information acquisition means. The information acquisition means is mounted on a mobile or fixed acoustic device and mainly acquires the user's voice in real time. The acquired voice is transmitted to a processing device using wireless communication technology. Specifically, wireless communication standards such as Bluetooth and Wi-Fi are used here.

[0097] The server first uses speech recognition software such as Google Cloud Speech-to-Text to process the audio, converting the audio data into text data. Next, it is translated into the target language via a translation tool such as the Google Cloud Translation API. Finally, the translated text data is converted back into audio data using a speech regeneration tool such as Amazon Polly.

[0098] The converted audio data is provided to the user through an audio supply means. Specifically, the user can listen to the audio in real time using earphones or speakers connected to their device.

[0099] For example, this invention is extremely useful when a tourist information robot interacts with tourists who speak different languages. When the robot hears a tourist's question, "What are the highlights of this place?", the server translates it to "What are the highlights of this place?" and immediately outputs it as audio, providing the tourist with information in the appropriate language.

[0100] An example of a prompt in a generative AI model is "Please translate the text from the original language to the target language." Using this prompt can yield highly accurate translation output.

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The device acquires the user's voice. When the user speaks into the microphone, the voice is captured as an analog signal. This analog signal is converted into digital data using digital signal processing technology and transmitted to the server via wireless communication.

[0104] Step 2:

[0105] The server converts received audio data into text data using speech recognition software. The input is digitized audio data, and the output is the corresponding string of characters. This conversion uses a generative AI model to extract audio features and convert the audio into text based on those features.

[0106] Step 3:

[0107] The server translates text data into the specified language using a translation device. The input is text data in the original language, and the output is the translated text data. The prompt is in the format of "Translate the text in the original language into the target language," and the generative AI model generates the optimal translation result.

[0108] Step 4:

[0109] The server converts the translated text data into audio data using a speech regeneration mechanism. The input is translated text data, and the output is audio data. In this process, natural-sounding speech is generated using speech synthesis technology and represented as sound waves.

[0110] Step 5:

[0111] The server sends the generated audio data to the terminal using an audio supply device. The terminal receives the transmitted audio data and plays it back to the user through earphones or speakers. This allows the user to listen to the translated audio in real time.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention is a system that integrates wireless earphones and a remote server to enable a more natural and contextually appropriate translation experience by performing text conversion, translation, speech synthesis, and emotion recognition of voice data. This system improves the quality of dialogue by translating the user's voice in real time and reflecting the user's emotional state in the translation.

[0114] Users wear wireless earphones and can freely speak different languages. Their speech is captured by the earphone's microphone and sent as audio data from the device to the server. The server converts the audio data into text data and then translates the text into the specified language. During this process, the server incorporates an emotion engine that detects the user's emotions from the audio data.

[0115] The emotion engine analyzes features such as tone, speed, and pitch from speech to identify emotional states such as anger, joy, and surprise. This emotion data is fed back into the translation process. For example, if a user is expressing dissatisfaction, the translation result is adjusted to use expressions appropriate to that emotion. By appropriately conveying emotional nuances, misunderstandings are reduced, and communication becomes smoother.

[0116] Furthermore, the server generates emotionally reflective speech using speech synthesis and transmits it to wireless earphones as translated speech. The user can receive this translated speech naturally, enabling smooth, back-and-forth conversation. For example, in a business meeting, when a user speaks enthusiastically about a proposal, the system corrects the translation to ensure that the emotion is accurately conveyed to the other party. In this way, the present invention supports communication that transcends language barriers and provides a smooth conversational environment by accurately conveying emotions.

[0117] The following describes the processing flow.

[0118] Step 1:

[0119] The user uses wireless earphones and begins speaking naturally in their own language. The earphones capture the voice through their built-in microphone, convert it into audio data, and transmit it to the device.

[0120] Step 2:

[0121] The terminal temporarily stores the acquired audio data and transmits it to the server via wireless communication. During this process, the data is compressed as needed for faster processing.

[0122] Step 3:

[0123] The server converts the received audio data into text using a speech recognition engine. This converted text is then used as input data for the next translation step.

[0124] Step 4:

[0125] The server activates an emotion engine to analyze the user's emotions from the voice data. It identifies the user's emotional state based on information such as voice tone, pitch, and speed.

[0126] Step 5:

[0127] When the server translates text data into the specified target language, it adjusts the translation results by taking into account the analyzed sentiment information. This adjustment ensures that the translated text appropriately reflects the user's intent and emotions.

[0128] Step 6:

[0129] The server uses a speech synthesis engine to convert the emotionally charged translated text into speech. The synthesized speech is then adjusted to sound natural while maintaining the original emotion.

[0130] Step 7:

[0131] The server sends the converted audio data back to the device. The device then transfers the received audio data to wireless earphones, allowing the user to listen to it in real time. This enables the user to smoothly share the translated audio with others.

[0132] (Example 2)

[0133] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0134] In communication between users who speak different languages, there is a challenge in accurately conveying not only the sound but also nuances, including emotions. In particular, if text translation remains mechanical and does not reflect the user's emotions, misunderstandings and inefficient communication can occur. Therefore, we want to improve the quality of speech translation and meet the demand for accurately conveying emotions.

[0135] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0136] In this invention, the server includes an acquisition means for acquiring audio, a conversion means for converting the acquired audio into text information, and an adjustment means for recognizing emotions from the audio and reflecting those emotions in the translated text. This enables translation with natural expressions that include emotions.

[0137] "Speech" refers to human speech and acoustic signals, which, once acquired, can be used as communication data.

[0138] "Acquisition means" refers to a device that collects audio using wireless earphones or similar devices and converts it into a digital signal.

[0139] A "conversion means" refers to a process or device that has the function of analyzing acquired audio and processing it as textual information.

[0140] "Textual information" refers to text data converted from speech, and serves as the basic data for language translation and sentiment analysis.

[0141] "Translation means" refers to the processing or equipment used to convert textual information written in one language into another language.

[0142] "Synthesis means" refers to the process or device for reconstructing translated text information as audio data.

[0143] "Adjustment means" refers to processes that modify the translated text based on information obtained through emotion recognition, thereby reflecting emotions.

[0144] A "transmission means" is a device that has the function of transferring data in order to provide the synthesized audio data to the user's hearing device.

[0145] "Emotion recognition" refers to the technology that analyzes characteristics such as tone, speed, and pitch from speech to identify the speaker's emotional state.

[0146] "Wireless communication" refers to methods of transmitting data to remote locations using technologies such as Bluetooth and Wi-Fi.

[0147] The system of this invention is primarily implemented using wireless earphones, a terminal, and a remote server. First, the user puts on the wireless earphones and inputs voice. This voice is acquired by the earphone's microphone, converted into a digital signal, and transmitted to the terminal. The terminal then transfers the digital voice signal to the server via wireless communication.

[0148] The server converts the acquired audio data into text information using the Google Cloud Speech-to-Text API or similar services. This conversion ensures the audio data is treated as text data. Next, the server translates the converted text data into the specified language using DeepL or other translation services.

[0149] Furthermore, an emotion engine on the server analyzes the audio data to recognize emotions from tone, speed, and pitch. This analysis utilizes technologies such as IBM Watson® Tone Analyzer. The recognized emotions are reflected in the translated text, and the translated text is modified by adjustment mechanisms. This process makes the translation more contextually appropriate.

[0150] Finally, the server converts the improved text information back into audio data using tools such as Amazon Polly or Google Cloud Text-to-Speech to generate synthesized speech. This synthesized speech is transmitted to the user's hearing device with low latency, allowing the user to receive it as a natural-sounding translation.

[0151] As a concrete example, if a user were to say "This proposal is groundbreaking" through this system during a business meeting, the system would translate it as "This proposal is groundbreaking" and play it back as an emotionally rich voice.

[0152] An example of a prompt using a generative AI model is, "Design and explain a system that translates speech in real time while considering the user's emotions." This prompt clarifies the user's intent, allowing the system to provide a translation that appropriately reflects those emotions.

[0153] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0154] Step 1:

[0155] The user wears wireless earphones to input voice. The earphone's microphone captures the user's voice and converts this audio signal from analog to digital. The acquired audio data is then transferred to the terminal. The input is the user's voice, and the output is a digital audio signal.

[0156] Step 2:

[0157] The terminal is responsible for transmitting the converted digital audio signal to the server via wireless communication. Specifically, it transfers the audio transmitted to the terminal via Bluetooth communication to the server via the internet using Wi-Fi or 4G / 5G networks. The input for this step is the digital audio signal, and the output is the data to be transferred to the server.

[0158] Step 3:

[0159] The server processes the received digital audio signal and converts it into text information using a speech recognition engine. Specifically, the Google Cloud Speech-to-Text API performs this conversion and outputs the audio data as text data. The input is the audio data sent to the server, and the output is the converted text data.

[0160] Step 4:

[0161] The server uses translation tools to translate the converted text data into the specified language. Translation services such as DeepL are utilized to translate the text data into different languages. The input for this step is text data, and the output is translated text data.

[0162] Step 5:

[0163] The server recognizes emotions from speech. It analyzes the tone, speed, and pitch, which are features of the speech data. IBM Watson Tone Analyzer performs this analysis, identifies the emotional state, and outputs the results. In this step, the input is the initial digital speech signal, and the output is the recognized emotion data.

[0164] Step 6:

[0165] The server adjusts the translated text based on recognized sentiment data. It makes adjustments to reflect the emotional content, correcting it to a natural expression that includes emotion. The input is the translated text data and sentiment data, and the output is the adjusted text data.

[0166] Step 7:

[0167] Finally, the server uses a speech synthesis engine to convert the adjusted text data back into speech data. Amazon Polly or Google Cloud Text-to-Speech are used to generate emotionally responsive synthesized speech. The input is adjusted text data, and the output is synthesized speech.

[0168] Step 8:

[0169] The server transmits the generated synthesized speech to the user with low latency. The transmitted speech is played back through wireless earphones, allowing the user to obtain a natural-sounding translation. The input is synthesized speech, and the output is the speech playback on the user's hearing device.

[0170] (Application Example 2)

[0171] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0172] In communication between people who speak different languages, the challenge lies not only in language barriers but also in the inability to properly convey emotional nuances. In particular, in everyday conversations such as those within the family, there is a need for methods that can accurately convey the speaker's feelings and intentions, rather than simply translating the language. Current translation systems can lead to misunderstandings because they fail to reflect emotions, and therefore need improvement.

[0173] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0174] In this invention, the server includes an acquisition means for acquiring voice data, a conversion means for converting the acquired voice data into encoded data, a language conversion means for translating the converted encoded data into another specified language, and a modification means for adjusting the translated encoded data based on recognized emotion attributes. This enables natural dialogue, including emotions and nuances, between users who speak different languages.

[0175] "Audio data" refers to audio information collected by the acquisition method, and includes the content of the user's speech.

[0176] "Encoded data" refers to data obtained by converting audio data into a different format using various conversion methods. This data forms the basis for translation and emotion recognition.

[0177] A "language conversion means" is an element that has the function of translating encoded data into another specified language.

[0178] "Emotional attributes" refer to information indicating the speaker's emotional state, extracted from the tone and pitch of speech analyzed by recognition tools.

[0179] "Correction measures" refer to functions for adjusting coded data translated based on recognized emotion attributes.

[0180] "Audio signal" refers to sound information sent to auditory devices, generated from encoded data using synthesis methods.

[0181] "User" refers to the person who communicates using the system and receives audio signals through an auditory device.

[0182] The system implementing this invention mainly consists of an auditory device, a terminal, and a remote server. When a user wears the auditory device and speaks, the audio data is transmitted to the server via the terminal.

[0183] On the server, first, the voice acquisition means receives voice data, and the conversion means converts it into encoded data. Next, the language conversion means converts the encoded data into another specified language. At this time, the recognition means analyzes emotional attributes from the tone and pitch of the voice, and the correction means adjusts the translation result based on this information. As a result, the synthesis means generates the final voice signal, which is transmitted to the user's hearing device with low latency.

[0184] This process can be sped up by utilizing cloud computing services. Specifically, it converts audio data into encoded data using Amazon Web Services' speech recognition API, performs language conversion using Google Translate's translation API, and performs emotion recognition using IBM Watson's sentiment analysis service. Finally, it performs speech synthesis using Amazon Polly.

[0185] For example, a robot can act as an intermediary between a Japanese-speaking child and an English-speaking parent within a family, accurately conveying the emotions embedded in the child's Japanese speech into English. The parent's English responses are also processed similarly, enabling smooth, two-way communication.

[0186] The example prompt shows that when a user says, "Can you tell me what happened at school today?", it is translated as "Tell me about what happened at school today?".

[0187] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0188] Step 1:

[0189] When a user speaks, audio data is captured by the microphone of the hearing device. The terminal transmits this audio data to the server using wireless communication. The input is the user's voice, and the output is the transfer of audio data to the server.

[0190] Step 2:

[0191] The server converts the received audio data into encoded data using speech recognition software, such as Amazon Web Services' speech recognition API. This process involves data processing that analyzes the audio frequency and waveform and converts it into text data. The input is audio data, and the output is encoded data.

[0192] Step 3:

[0193] The converted encoded data is translated into the specified language using Google Translate's translation API. The server performs the data conversion between languages ​​through this process. The input is encoded data, and the output is translated encoded data.

[0194] Step 4:

[0195] The server uses IBM Watson's sentiment analysis service to analyze the speaker's sentiment attributes from the translated encoded data. This process analyzes features such as speech tone, pitch, and speed to calculate the sentiment attributes. The input is the translated encoded data, and the output is the identified sentiment attributes.

[0196] Step 5:

[0197] The correction mechanism allows the server to adjust the translation results based on sentiment attributes. Sentiment data is used to fine-tune the translated text data to reflect the desired sentiment. The input consists of sentiment attributes and translated encoded data, while the output is the adjusted encoded data.

[0198] Step 6:

[0199] Using Amazon Polly as the synthesis method, the server converts the tuned encoded data into a speech signal. The synthesized speech reflects emotions, resulting in natural-sounding dialogue. The input is tuned encoded data, and the output is a synthesized speech signal.

[0200] Step 7:

[0201] Ultimately, the server transmits the audio signal to the hearing device with low latency, and the user receives it. It becomes possible to hear the translated audio instantly, allowing for smooth conversations even between speakers of different languages. The input is the synthesized audio signal, and the output is its delivery to the user's hearing device.

[0202] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0203] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0204] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0205] [Second Embodiment]

[0206] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0207] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0208] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0209] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0210] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0211] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0212] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0213] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0214] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0215] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0216] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0217] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0218] This invention relates to a real-time translation system combining wireless earphones and a server. This system uses a speech acquisition means to acquire the user's speech and a text conversion means to convert that speech into text. The converted text is then translated on the server side by a translation means into the language specified by the server.

[0219] The server converts the translated text into audio data using speech synthesis technology and transmits that audio to wireless earphones in real time. By listening to this audio through the earphones, the user can hear what they have said played back in another language, and also hear what others have said in a translated state.

[0220] As a concrete example, when a user speaks Japanese, the wireless earphones capture the speech and transmit it to a server. The server converts the received audio into text, translates it into, for example, English, and then uses speech synthesis to send the English audio data back to the wireless earphones. As a result, even if the user speaks in Japanese, the content will be automatically translated into English and conveyed to the person speaking another language. This makes it possible to provide real-time, seamless communication between different languages.

[0221] In addition, wireless earphones, as terminals, are lightweight and highly portable, eliminating the need for users to carry around bulky traditional translation devices. Furthermore, by utilizing high-performance AI on a server, more accurate translations can be performed, providing users with a more natural conversational experience. This system configuration significantly improves the practicality of real-time translation, and its use is expected to expand to a wide range of situations.

[0222] The following describes the processing flow.

[0223] Step 1:

[0224] The user wears wireless earphones and speaks what they want to say in their own language. The earphone's microphone captures the audio and processes it to send it to the device as digital data.

[0225] Step 2:

[0226] The terminal receives the acquired voice data and transfers it to the server using wireless communication. During this process, the voice data may be compressed or encrypted to minimize latency.

[0227] Step 3:

[0228] The server analyzes the received audio data and converts it into text data using speech recognition technology. This converted text is then used as input data for subsequent translation processes.

[0229] Step 4:

[0230] The server translates text data into the specified target language. By using an AI-based translation engine, it performs context-aware, highly accurate translations.

[0231] Step 5:

[0232] The server converts the translated text into speech data using speech synthesis technology. This speech data is generated in a form that closely resembles natural speech and is in a format that users can easily understand.

[0233] Step 6:

[0234] The server sends audio data to the terminal. During transmission, the audio data is appropriately formatted to maintain sound quality and enable real-time playback.

[0235] Step 7:

[0236] The device receives audio data transmitted from the server and plays it back to the user's ears through the speaker in the wireless earphones. By listening to the translated audio, the user can understand the other person's language and communicate in real time.

[0237] (Example 1)

[0238] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0239] This real-time translation system aims to overcome challenges in facilitating communication between different languages, such as the lack of portability of conventional devices, limitations in translation accuracy, and the disruption of natural conversation due to delays in audio data.

[0240] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0241] In this invention, the server includes means for acquiring audio signals using an acoustic acquisition device and converting them into text data using a character conversion device, means for translating them into another specified language using a translation device, and means for converting them into audio data using an acoustic synthesis device. This makes it possible to provide real-time and natural communication between different languages ​​through a highly portable device.

[0242] An "acoustic acquisition device" is a device that acquires audio signals and converts them into a processable data format.

[0243] A "character conversion device" is a device that analyzes acquired audio signals and converts them into corresponding character data.

[0244] A "translation device" is a device used to convert text data into another specified language.

[0245] A "sound synthesis device" is a device that converts translated text data back into sound data and outputs it in a playable format.

[0246] A "voice transmission device" is a device that provides converted audio data to the user's hearing device.

[0247] "Wireless communication" is a technology that transmits data without using an energy medium, and generally refers to methods that use radio waves.

[0248] "Remote computing resources" refer to computing devices and processing power located in physically distant locations that are accessible via a network.

[0249] This invention is a system for real-time communication between different languages ​​and includes a series of devices for acquiring, translating, and reproducing audio signals. The user speaks using wireless earphones as an audio acquisition device. The earphones acquire the audio signal and transmit this data to a server via a terminal.

[0250] The server converts the received audio data into text data using Google Cloud Speech-to-Text or an equivalent speech recognition API. Next, it translates this text data into the specified language using a generative AI model. OpenAI's GPT is one example of an AI model that can be used here. For translation, a prompt like the following can be used: "Translate the following Japanese into English: 'It's a nice day today.'"

[0251] The translated text data is synthesized into audio data using Amazon Polly or the Google Text-to-Speech API. The server transmits this audio data to the user's wireless earphones with low latency, allowing the user to hear the translated audio. This enables users who speak different languages ​​to communicate naturally.

[0252] Through the process described above, this system provides highly portable, accurate, and real-time translation, enabling users to enjoy comfortable international communication. For example, a user's Japanese utterance, "Ohayou gozaimasu" (Good morning), is transmitted to the other party in real time via the server process as "Good morning" in English. This system supports efficient dialogue in a variety of international situations.

[0253] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0254] Step 1:

[0255] The user speaks through wireless earphones. This audio signal is directly captured by the earphone's microphone. The input is the user's spoken audio signal, and the output is digitized audio data. At this stage, the audio is converted from analog to digital and temporarily stored within the device.

[0256] Step 2:

[0257] The terminal transmits digitized audio data to the server via wireless communication technology (e.g., Bluetooth). The input is the digital audio data stored on the terminal, and the output is the audio data sent to the server. In this process, the data is divided into packets, error checked, and then forwarded to the server.

[0258] Step 3:

[0259] The server processes the received audio data and converts it into text data using a speech recognition API (e.g., acoustic recognition technology). The input is the digital audio data received by the server, and the output is the converted text data. At this stage, the audio is analyzed to identify the exact string from the phonemes in the speech.

[0260] Step 4:

[0261] The server uses a generative AI model to translate acquired text data into a specified language. The input is text data, and the output is translated text data. A prompt "Translate the following text" is sent to the generative AI model, and the translated text is obtained as a response.

[0262] Step 5:

[0263] The server converts the translated text data into speech data using a speech synthesis API (e.g., audio synthesis technology). The input is the translated text data, and the output is the synthesized speech data. At this stage, the speech waveform is synthesized and regenerated as natural speech.

[0264] Step 6:

[0265] The server transmits synthesized audio data to the terminal with low latency using wireless communication technology. The input is the synthesized audio data on the server, and the output is the received audio data. The data necessary for playback is transmitted to the terminal in real time.

[0266] Step 7:

[0267] The device outputs the received audio data to the user's wireless earphones. The input is the received audio data, and the output is the audio the user hears. The user can hear the translated audio in real time and communicate smoothly with others.

[0268] (Application Example 1)

[0269] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0270] There is a need to provide means for people who speak different languages ​​to communicate smoothly. However, existing translation devices are large and inconvenient to carry, making them difficult to use in daily life or while traveling. Furthermore, there is a lack of systems that can provide a smooth and natural conversation experience. To solve these problems, a new translation system is needed.

[0271] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0272] In this invention, the server includes an information acquisition means for acquiring audio data, a character conversion means for converting the acquired audio data into character data, and a translation device for converting the converted character data into another specified language. This enables smooth, real-time communication between people who speak different languages.

[0273] "Information acquisition means" refers to devices or processes that have the function of collecting audio or data.

[0274] A "text conversion means" refers to a device or process that has the function of converting acquired audio data into text information.

[0275] A "translation device" is a device or process that has the function of converting textual information from one language to another.

[0276] "Speech regeneration means" refers to a device or process that has the function of converting text information into speech data and outputting it.

[0277] "Voice supply means" refers to a device or process that has the function of providing converted voice data to a user.

[0278] Wireless communication is a technology that uses radio waves to transfer data without using cables.

[0279] A "processing device" refers to a computer system or server used for data processing.

[0280] "Aircraft" refers to the entire device that is equipped with voice acquisition means and is used to process or output information.

[0281] To implement this invention, first, it is necessary to prepare a device for collecting sound as information acquisition means. The information acquisition means is installed in a moving body or a fixed acoustic device, and mainly acquires the user's voice in real time. The acquired voice is transmitted to the processing device using wireless communication technology. Here, specifically, Bluetooth or Wi-Fi, which are wireless communication standards, are utilized.

[0282] First, the server uses speech recognition software such as Google Cloud Speech-to-Text to process the voice and convert the voice data into character data. Next, it is translated into the target language through a translation device such as the Google Cloud Translation API. Then, the translated character data is converted back into voice data using voice regeneration means such as Amazon Polly.

[0283] The converted voice data is provided to the user through the voice supply means. Specifically, by using earphones or speakers connected to the user's terminal, the voice can be heard in real time.

[0284] For example, when a tourist guide robot interacts with tourists speaking different languages, this invention is very useful. When the robot hears the question "What are the highlights of this place?" from the tourist, it is translated into "What are the highlights of this place?" on the server and immediately output as voice, providing information to the tourist in an appropriate language.

[0285] As an example of the prompt sentence in the generation AI model, it can be input in the form of "Please translate the text in the original language to the target language." By using this prompt, an output with high translation accuracy can be obtained.

[0286] The flow of the specific process in Application Example 1 will be described using FIG. 12.

[0287] Step 1:

[0288] The device acquires the user's voice. When the user speaks into the microphone, the voice is captured as an analog signal. This analog signal is converted into digital data using digital signal processing technology and transmitted to the server via wireless communication.

[0289] Step 2:

[0290] The server converts received audio data into text data using speech recognition software. The input is digitized audio data, and the output is the corresponding string of characters. This conversion uses a generative AI model to extract audio features and convert the audio into text based on those features.

[0291] Step 3:

[0292] The server translates text data into the specified language using a translation device. The input is text data in the original language, and the output is the translated text data. The prompt is in the format of "Translate the text in the original language into the target language," and the generative AI model generates the optimal translation result.

[0293] Step 4:

[0294] The server converts the translated text data into audio data using a speech regeneration mechanism. The input is translated text data, and the output is audio data. In this process, natural-sounding speech is generated using speech synthesis technology and represented as sound waves.

[0295] Step 5:

[0296] The server sends the generated audio data to the terminal using an audio supply device. The terminal receives the transmitted audio data and plays it back to the user through earphones or speakers. This allows the user to listen to the translated audio in real time.

[0297] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0298] This invention is a system that integrates wireless earphones and a remote server to enable a more natural and contextually appropriate translation experience by performing text conversion, translation, speech synthesis, and emotion recognition of voice data. This system improves the quality of dialogue by translating the user's voice in real time and reflecting the user's emotional state in the translation.

[0299] Users wear wireless earphones and can freely speak different languages. Their speech is captured by the earphone's microphone and sent as audio data from the device to the server. The server converts the audio data into text data and then translates the text into the specified language. During this process, the server incorporates an emotion engine that detects the user's emotions from the audio data.

[0300] The emotion engine analyzes features such as tone, speed, and pitch from speech to identify emotional states such as anger, joy, and surprise. This emotion data is fed back into the translation process. For example, if a user is expressing dissatisfaction, the translation result is adjusted to use expressions appropriate to that emotion. By appropriately conveying emotional nuances, misunderstandings are reduced, and communication becomes smoother.

[0301] Furthermore, the server generates emotionally reflective speech using speech synthesis and transmits it to wireless earphones as translated speech. The user can receive this translated speech naturally, enabling smooth, back-and-forth conversation. For example, in a business meeting, when a user speaks enthusiastically about a proposal, the system corrects the translation to ensure that the emotion is accurately conveyed to the other party. In this way, the present invention supports communication that transcends language barriers and provides a smooth conversational environment by accurately conveying emotions.

[0302] The processing flow will be described below.

[0303] Step 1:

[0304] The user starts speaking naturally in their own language using wireless earphones. The earphones acquire the voice through the built-in microphone, convert it into voice data, and transmit it to the terminal.

[0305] Step 2:

[0306] The terminal temporarily stores the acquired voice data and transmits it to the server via wireless communication. The data in this process is appropriately compressed for prompt processing.

[0307] Step 3:

[0308] The server converts the received voice data into text using a voice recognition engine. This converted text is treated as input data for the next translation step.

[0309] Step 4:

[0310] The server operates an emotion engine to analyze the user's emotion from the voice data. From information such as the tone, pitch, and speed of the voice, it identifies what kind of emotional state the user is in.

[0311] Step 5:

[0312] When the server translates the text data into the specified target language, it adjusts the translation result in consideration of the analyzed emotion information. This adjustment ensures that the translated text appropriately reflects the user's intention and emotion.

[0313] Step 6:

[0314] The server uses a speech synthesis engine to convert the emotionally charged translated text into speech. The synthesized speech is then adjusted to sound natural while maintaining the original emotion.

[0315] Step 7:

[0316] The server sends the converted audio data back to the device. The device then transfers the received audio data to wireless earphones, allowing the user to listen to it in real time. This enables the user to smoothly share the translated audio with others.

[0317] (Example 2)

[0318] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0319] In communication between users who speak different languages, there is a challenge in accurately conveying not only the sound but also nuances, including emotions. In particular, if text translation remains mechanical and does not reflect the user's emotions, misunderstandings and inefficient communication can occur. Therefore, we want to improve the quality of speech translation and meet the demand for accurately conveying emotions.

[0320] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0321] In this invention, the server includes an acquisition means for acquiring audio, a conversion means for converting the acquired audio into text information, and an adjustment means for recognizing emotions from the audio and reflecting those emotions in the translated text. This enables translation with natural expressions that include emotions.

[0322] "Speech" refers to human speech and acoustic signals, which, once acquired, can be used as communication data.

[0323] "Acquisition means" refers to a device that collects audio using wireless earphones or similar devices and converts it into a digital signal.

[0324] A "conversion means" refers to a process or device that has the function of analyzing acquired audio and processing it as textual information.

[0325] "Textual information" refers to text data converted from speech, and serves as the basic data for language translation and sentiment analysis.

[0326] "Translation means" refers to the processing or equipment used to convert textual information written in one language into another language.

[0327] "Synthesis means" refers to the process or device for reconstructing translated text information as audio data.

[0328] "Adjustment means" refers to processes that modify the translated text based on information obtained through emotion recognition, thereby reflecting emotions.

[0329] A "transmission means" is a device that has the function of transferring data in order to provide the synthesized audio data to the user's hearing device.

[0330] "Emotion recognition" refers to the technology that analyzes characteristics such as tone, speed, and pitch from speech to identify the speaker's emotional state.

[0331] "Wireless communication" refers to methods of transmitting data to remote locations using technologies such as Bluetooth and Wi-Fi.

[0332] The system of this invention is primarily implemented using wireless earphones, a terminal, and a remote server. First, the user puts on the wireless earphones and inputs voice. This voice is acquired by the earphone's microphone, converted into a digital signal, and transmitted to the terminal. The terminal then transfers the digital voice signal to the server via wireless communication.

[0333] The server converts the acquired audio data into text information using the Google Cloud Speech-to-Text API or similar services. This conversion ensures the audio data is treated as text data. Next, the server translates the converted text data into the specified language using DeepL or other translation services.

[0334] Furthermore, an emotion engine on the server analyzes the audio data to recognize emotions from tone, speed, and pitch. This analysis utilizes technologies such as IBM Watson Tone Analyzer. The recognized emotions are reflected in the translated text, and the translated text is modified by adjustment mechanisms. This process makes the translation more contextually appropriate.

[0335] Finally, the server converts the improved text information back into audio data using tools such as Amazon Polly or Google Cloud Text-to-Speech to generate synthesized speech. This synthesized speech is transmitted to the user's hearing device with low latency, allowing the user to receive it as a natural-sounding translation.

[0336] As a concrete example, if a user were to say "This proposal is groundbreaking" through this system during a business meeting, the system would translate it as "This proposal is groundbreaking" and play it back as an emotionally rich voice.

[0337] An example of a prompt using a generative AI model is, "Design and explain a system that translates speech in real time while considering the user's emotions." This prompt clarifies the user's intent, allowing the system to provide a translation that appropriately reflects those emotions.

[0338] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0339] Step 1:

[0340] The user wears wireless earphones to input voice. The earphone's microphone captures the user's voice and converts this audio signal from analog to digital. The acquired audio data is then transferred to the terminal. The input is the user's voice, and the output is a digital audio signal.

[0341] Step 2:

[0342] The terminal is responsible for transmitting the converted digital audio signal to the server via wireless communication. Specifically, it transfers the audio transmitted to the terminal via Bluetooth communication to the server via the internet using Wi-Fi or 4G / 5G networks. The input for this step is the digital audio signal, and the output is the data to be transferred to the server.

[0343] Step 3:

[0344] The server processes the received digital audio signal and converts it into text information using a speech recognition engine. Specifically, the Google Cloud Speech-to-Text API performs this conversion and outputs the audio data as text data. The input is the audio data sent to the server, and the output is the converted text data.

[0345] Step 4:

[0346] The server uses translation tools to translate the converted text data into the specified language. Translation services such as DeepL are utilized to translate the text data into different languages. The input for this step is text data, and the output is translated text data.

[0347] Step 5:

[0348] The server recognizes emotions from speech. It analyzes the tone, speed, and pitch, which are features of the speech data. IBM Watson Tone Analyzer performs this analysis, identifies the emotional state, and outputs the results. In this step, the input is the initial digital speech signal, and the output is the recognized emotion data.

[0349] Step 6:

[0350] The server adjusts the translated text based on recognized sentiment data. It makes adjustments to reflect the emotional content, correcting it to a natural expression that includes emotion. The input is the translated text data and sentiment data, and the output is the adjusted text data.

[0351] Step 7:

[0352] Finally, the server uses a speech synthesis engine to convert the adjusted text data back into speech data. Amazon Polly or Google Cloud Text-to-Speech are used to generate emotionally responsive synthesized speech. The input is adjusted text data, and the output is synthesized speech.

[0353] Step 8:

[0354] The server transmits the generated synthesized speech to the user with low latency. The transmitted speech is played back through wireless earphones, allowing the user to obtain a natural-sounding translation. The input is synthesized speech, and the output is the speech playback on the user's hearing device.

[0355] (Application Example 2)

[0356] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0357] In communication between people who speak different languages, the challenge lies not only in language barriers but also in the inability to properly convey emotional nuances. In particular, in everyday conversations such as those within the family, there is a need for methods that can accurately convey the speaker's feelings and intentions, rather than simply translating the language. Current translation systems can lead to misunderstandings because they fail to reflect emotions, and therefore need improvement.

[0358] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0359] In this invention, the server includes an acquisition means for acquiring voice data, a conversion means for converting the acquired voice data into encoded data, a language conversion means for translating the converted encoded data into another specified language, and a modification means for adjusting the translated encoded data based on recognized emotion attributes. This enables natural dialogue, including emotions and nuances, between users who speak different languages.

[0360] "Audio data" refers to audio information collected by the acquisition method, and includes the content of the user's speech.

[0361] "Encoded data" refers to data obtained by converting audio data into a different format using various conversion methods. This data forms the basis for translation and emotion recognition.

[0362] A "language conversion means" is an element that has the function of translating encoded data into another specified language.

[0363] "Emotional attributes" refer to information indicating the speaker's emotional state, extracted from the tone and pitch of speech analyzed by recognition tools.

[0364] "Correction measures" refer to functions for adjusting coded data translated based on recognized emotion attributes.

[0365] "Audio signal" refers to sound information sent to auditory devices, generated from encoded data using synthesis methods.

[0366] "User" refers to the person who communicates using the system and receives audio signals through an auditory device.

[0367] The system implementing this invention mainly consists of an auditory device, a terminal, and a remote server. When a user wears the auditory device and speaks, the audio data is transmitted to the server via the terminal.

[0368] On the server, first, the voice acquisition means receives voice data, and the conversion means converts it into encoded data. Next, the language conversion means converts the encoded data into another specified language. At this time, the recognition means analyzes emotional attributes from the tone and pitch of the voice, and the correction means adjusts the translation result based on this information. As a result, the synthesis means generates the final voice signal, which is transmitted to the user's hearing device with low latency.

[0369] This process can be sped up by utilizing cloud computing services. Specifically, it converts audio data into encoded data using Amazon Web Services' speech recognition API, performs language conversion using Google Translate's translation API, and performs emotion recognition using IBM Watson's sentiment analysis service. Finally, it performs speech synthesis using Amazon Polly.

[0370] For example, a robot can act as an intermediary between a Japanese-speaking child and an English-speaking parent within a family, accurately conveying the emotions embedded in the child's Japanese speech into English. The parent's English responses are also processed similarly, enabling smooth, two-way communication.

[0371] The example prompt shows that when a user says, "Can you tell me what happened at school today?", it is translated as "Tell me about what happened at school today?".

[0372] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0373] Step 1:

[0374] When a user speaks, audio data is captured by the microphone of the hearing device. The terminal transmits this audio data to the server using wireless communication. The input is the user's voice, and the output is the transfer of audio data to the server.

[0375] Step 2:

[0376] The server converts the received audio data into encoded data using speech recognition software, such as Amazon Web Services' speech recognition API. This process involves data processing that analyzes the audio frequency and waveform and converts it into text data. The input is audio data, and the output is encoded data.

[0377] Step 3:

[0378] The converted encoded data is translated into the specified language using Google Translate's translation API. The server performs the data conversion between languages ​​through this process. The input is encoded data, and the output is translated encoded data.

[0379] Step 4:

[0380] The server uses IBM Watson's sentiment analysis service to analyze the speaker's sentiment attributes from the translated encoded data. This process analyzes features such as speech tone, pitch, and speed to calculate the sentiment attributes. The input is the translated encoded data, and the output is the identified sentiment attributes.

[0381] Step 5:

[0382] The correction mechanism allows the server to adjust the translation results based on sentiment attributes. Sentiment data is used to fine-tune the translated text data to reflect the desired sentiment. The input consists of sentiment attributes and translated encoded data, while the output is the adjusted encoded data.

[0383] Step 6:

[0384] Using Amazon Polly as the synthesis method, the server converts the tuned encoded data into a speech signal. The synthesized speech reflects emotions, resulting in natural-sounding dialogue. The input is tuned encoded data, and the output is a synthesized speech signal.

[0385] Step 7:

[0386] Ultimately, the server transmits the audio signal to the hearing device with low latency, and the user receives it. It becomes possible to hear the translated audio instantly, allowing for smooth conversations even between speakers of different languages. The input is the synthesized audio signal, and the output is its delivery to the user's hearing device.

[0387] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0388] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0389] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0390] [Third Embodiment]

[0391] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0392] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0393] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0394] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0395] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0396] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0397] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0398] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0399] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0400] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0401] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0402] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0403] This invention relates to a real-time translation system combining wireless earphones and a server. This system uses a speech acquisition means to acquire the user's speech and a text conversion means to convert that speech into text. The converted text is then translated on the server side by a translation means into the language specified by the server.

[0404] The server converts the translated text into audio data using speech synthesis technology and transmits that audio to wireless earphones in real time. By listening to this audio through the earphones, the user can hear what they have said played back in another language, and also hear what others have said in a translated state.

[0405] As a concrete example, when a user speaks Japanese, the wireless earphones capture the speech and transmit it to a server. The server converts the received audio into text, translates it into, for example, English, and then uses speech synthesis to send the English audio data back to the wireless earphones. As a result, even if the user speaks in Japanese, the content will be automatically translated into English and conveyed to the person speaking another language. This makes it possible to provide real-time, seamless communication between different languages.

[0406] In addition, wireless earphones, as terminals, are lightweight and highly portable, eliminating the need for users to carry around bulky traditional translation devices. Furthermore, by utilizing high-performance AI on a server, more accurate translations can be performed, providing users with a more natural conversational experience. This system configuration significantly improves the practicality of real-time translation, and its use is expected to expand to a wide range of situations.

[0407] The following describes the processing flow.

[0408] Step 1:

[0409] The user wears wireless earphones and speaks what they want to say in their own language. The earphone's microphone captures the audio and processes it to send it to the device as digital data.

[0410] Step 2:

[0411] The terminal receives the acquired voice data and transfers it to the server using wireless communication. During this process, the voice data may be compressed or encrypted to minimize latency.

[0412] Step 3:

[0413] The server analyzes the received audio data and converts it into text data using speech recognition technology. This converted text is then used as input data for subsequent translation processes.

[0414] Step 4:

[0415] The server translates text data into the specified target language. By using an AI-based translation engine, it performs context-aware, highly accurate translations.

[0416] Step 5:

[0417] The server converts the translated text into speech data using speech synthesis technology. This speech data is generated in a form that closely resembles natural speech and is in a format that users can easily understand.

[0418] Step 6:

[0419] The server sends audio data to the terminal. During transmission, the audio data is appropriately formatted to maintain sound quality and enable real-time playback.

[0420] Step 7:

[0421] The device receives audio data transmitted from the server and plays it back to the user's ears through the speaker in the wireless earphones. By listening to the translated audio, the user can understand the other person's language and communicate in real time.

[0422] (Example 1)

[0423] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0424] This real-time translation system aims to overcome challenges in facilitating communication between different languages, such as the lack of portability of conventional devices, limitations in translation accuracy, and the disruption of natural conversation due to delays in audio data.

[0425] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0426] In this invention, the server includes means for acquiring audio signals using an acoustic acquisition device and converting them into text data using a character conversion device, means for translating them into another specified language using a translation device, and means for converting them into audio data using an acoustic synthesis device. This makes it possible to provide real-time and natural communication between different languages ​​through a highly portable device.

[0427] An "acoustic acquisition device" is a device that acquires audio signals and converts them into a processable data format.

[0428] A "character conversion device" is a device that analyzes acquired audio signals and converts them into corresponding character data.

[0429] A "translation device" is a device used to convert text data into another specified language.

[0430] A "sound synthesis device" is a device that converts translated text data back into sound data and outputs it in a playable format.

[0431] A "voice transmission device" is a device that provides converted audio data to the user's hearing device.

[0432] "Wireless communication" is a technology that transmits data without using an energy medium, and generally refers to methods that use radio waves.

[0433] "Remote computing resources" refer to computing devices and processing power located in physically distant locations that are accessible via a network.

[0434] This invention is a system for real-time communication between different languages ​​and includes a series of devices for acquiring, translating, and reproducing audio signals. The user speaks using wireless earphones as an audio acquisition device. The earphones acquire the audio signal and transmit this data to a server via a terminal.

[0435] The server converts the received audio data into text data using Google Cloud Speech-to-Text or an equivalent speech recognition API. Next, it translates this text data into the specified language using a generative AI model. OpenAI's GPT is one example of an AI model that can be used here. For translation, a prompt like the following can be used: "Translate the following Japanese into English: 'It's a nice day today.'"

[0436] The translated text data is synthesized into audio data using Amazon Polly or the Google Text-to-Speech API. The server transmits this audio data to the user's wireless earphones with low latency, allowing the user to hear the translated audio. This enables users who speak different languages ​​to communicate naturally.

[0437] Through the process described above, this system provides highly portable, accurate, and real-time translation, enabling users to enjoy comfortable international communication. For example, a user's Japanese utterance, "Ohayou gozaimasu" (Good morning), is transmitted to the other party in real time via the server process as "Good morning" in English. This system supports efficient dialogue in a variety of international situations.

[0438] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0439] Step 1:

[0440] The user speaks through wireless earphones. This audio signal is directly captured by the earphone's microphone. The input is the user's spoken audio signal, and the output is digitized audio data. At this stage, the audio is converted from analog to digital and temporarily stored within the device.

[0441] Step 2:

[0442] The terminal transmits digitized audio data to the server via wireless communication technology (e.g., Bluetooth). The input is the digital audio data stored on the terminal, and the output is the audio data sent to the server. In this process, the data is divided into packets, error checked, and then forwarded to the server.

[0443] Step 3:

[0444] The server processes the received audio data and converts it into text data using a speech recognition API (e.g., acoustic recognition technology). The input is the digital audio data received by the server, and the output is the converted text data. At this stage, the audio is analyzed to identify the exact string from the phonemes in the speech.

[0445] Step 4:

[0446] The server uses a generative AI model to translate acquired text data into a specified language. The input is text data, and the output is translated text data. A prompt "Translate the following text" is sent to the generative AI model, and the translated text is obtained as a response.

[0447] Step 5:

[0448] The server converts the translated text data into speech data using a speech synthesis API (e.g., audio synthesis technology). The input is the translated text data, and the output is the synthesized speech data. At this stage, the speech waveform is synthesized and regenerated as natural speech.

[0449] Step 6:

[0450] The server transmits synthesized audio data to the terminal with low latency using wireless communication technology. The input is the synthesized audio data on the server, and the output is the received audio data. The data necessary for playback is transmitted to the terminal in real time.

[0451] Step 7:

[0452] The device outputs the received audio data to the user's wireless earphones. The input is the received audio data, and the output is the audio the user hears. The user can hear the translated audio in real time and communicate smoothly with others.

[0453] (Application Example 1)

[0454] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0455] There is a need to provide means for people who speak different languages ​​to communicate smoothly. However, existing translation devices are large and inconvenient to carry, making them difficult to use in daily life or while traveling. Furthermore, there is a lack of systems that can provide a smooth and natural conversation experience. To solve these problems, a new translation system is needed.

[0456] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0457] In this invention, the server includes an information acquisition means for acquiring audio data, a character conversion means for converting the acquired audio data into character data, and a translation device for converting the converted character data into another specified language. This enables smooth, real-time communication between people who speak different languages.

[0458] "Information acquisition means" refers to devices or processes that have the function of collecting audio or data.

[0459] A "text conversion means" refers to a device or process that has the function of converting acquired audio data into text information.

[0460] A "translation device" is a device or process that has the function of converting textual information from one language to another.

[0461] "Speech regeneration means" refers to a device or process that has the function of converting text information into speech data and outputting it.

[0462] "Voice supply means" refers to a device or process that has the function of providing converted voice data to a user.

[0463] Wireless communication is a technology that uses radio waves to transfer data without using cables.

[0464] A "processing device" refers to a computer system or server used for data processing.

[0465] "Aircraft" refers to the entire device that is equipped with voice acquisition means and is used to process or output information.

[0466] To implement this invention, it is first necessary to prepare a device for collecting voice as an information acquisition means. The information acquisition means is mounted on a mobile or fixed acoustic device and mainly acquires the user's voice in real time. The acquired voice is transmitted to a processing device using wireless communication technology. Specifically, wireless communication standards such as Bluetooth and Wi-Fi are used here.

[0467] The server first uses speech recognition software such as Google Cloud Speech-to-Text to process the audio, converting the audio data into text data. Next, it is translated into the target language via a translation tool such as the Google Cloud Translation API. Finally, the translated text data is converted back into audio data using a speech regeneration tool such as Amazon Polly.

[0468] The converted audio data is provided to the user through an audio supply means. Specifically, the user can listen to the audio in real time using earphones or speakers connected to their device.

[0469] For example, this invention is extremely useful when a tourist information robot interacts with tourists who speak different languages. When the robot hears a tourist's question, "What are the highlights of this place?", the server translates it to "What are the highlights of this place?" and immediately outputs it as audio, providing the tourist with information in the appropriate language.

[0470] An example of a prompt in a generative AI model is "Please translate the text from the original language to the target language." Using this prompt can yield highly accurate translation output.

[0471] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0472] Step 1:

[0473] The device acquires the user's voice. When the user speaks into the microphone, the voice is captured as an analog signal. This analog signal is converted into digital data using digital signal processing technology and transmitted to the server via wireless communication.

[0474] Step 2:

[0475] The server converts received audio data into text data using speech recognition software. The input is digitized audio data, and the output is the corresponding string of characters. This conversion uses a generative AI model to extract audio features and convert the audio to text based on those features.

[0476] Step 3:

[0477] The server translates text data into the specified language using a translation device. The input is text data in the original language, and the output is the translated text data. The prompt is in the format of "Translate the text in the original language into the target language," and the generative AI model generates the optimal translation result.

[0478] Step 4:

[0479] The server converts the translated text data into audio data using a speech regeneration mechanism. The input is translated text data, and the output is audio data. In this process, natural-sounding speech is generated using speech synthesis technology and represented as sound waves.

[0480] Step 5:

[0481] The server transmits the generated audio data to the terminal using an audio supply device. The terminal receives the transmitted audio data and plays it back to the user through earphones or speakers. This allows the user to listen to the translated audio in real time.

[0482] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0483] This invention is a system that integrates wireless earphones and a remote server to enable a more natural and contextually appropriate translation experience by performing text conversion, translation, speech synthesis, and emotion recognition of voice data. This system improves the quality of dialogue by translating the user's voice in real time and reflecting the user's emotional state in the translation.

[0484] Users wear wireless earphones and can freely speak different languages. Their speech is captured by the earphone's microphone and sent as audio data from the device to the server. The server converts the audio data into text data and then translates the text into the specified language. During this process, the server incorporates an emotion engine that detects the user's emotions from the audio data.

[0485] The emotion engine analyzes features such as tone, speed, and pitch from speech to identify emotional states such as anger, joy, and surprise. This emotion data is fed back into the translation process. For example, if a user is expressing dissatisfaction, the translation result is adjusted to use expressions appropriate to that emotion. By appropriately conveying emotional nuances, misunderstandings are reduced, and communication becomes smoother.

[0486] Furthermore, the server generates emotionally reflective speech using speech synthesis and transmits it to wireless earphones as translated speech. The user can receive this translated speech naturally, enabling smooth, back-and-forth conversation. For example, in a business meeting, when a user speaks enthusiastically about a proposal, the system corrects the translation to ensure that the emotion is accurately conveyed to the other party. In this way, the present invention supports communication that transcends language barriers and provides a smooth conversational environment by accurately conveying emotions.

[0487] The following describes the processing flow.

[0488] Step 1:

[0489] The user uses wireless earphones and begins speaking naturally in their own language. The earphones capture the voice through their built-in microphone, convert it into audio data, and transmit it to the device.

[0490] Step 2:

[0491] The terminal temporarily stores the acquired audio data and transmits it to the server via wireless communication. During this process, the data is compressed as needed for faster processing.

[0492] Step 3:

[0493] The server converts the received audio data into text using a speech recognition engine. This converted text is then used as input data for the next translation step.

[0494] Step 4:

[0495] The server activates an emotion engine to analyze the user's emotions from the voice data. It identifies the user's emotional state based on information such as voice tone, pitch, and speed.

[0496] Step 5:

[0497] When the server translates text data into the specified target language, it adjusts the translation results by taking into account the analyzed sentiment information. This adjustment ensures that the translated text appropriately reflects the user's intent and emotions.

[0498] Step 6:

[0499] The server uses a speech synthesis engine to convert the emotionally charged translated text into speech. The synthesized speech is then adjusted to sound natural while maintaining the original emotion.

[0500] Step 7:

[0501] The server sends the converted audio data back to the device. The device then transfers the received audio data to wireless earphones, allowing the user to listen to it in real time. This enables the user to smoothly share the translated audio with others.

[0502] (Example 2)

[0503] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0504] In communication between users who speak different languages, there is a challenge in accurately conveying not only the sound but also nuances, including emotions. In particular, if text translation remains mechanical and does not reflect the user's emotions, misunderstandings and inefficient communication can occur. Therefore, we want to improve the quality of speech translation and meet the demand for accurately conveying emotions.

[0505] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0506] In this invention, the server includes an acquisition means for acquiring audio, a conversion means for converting the acquired audio into text information, and an adjustment means for recognizing emotions from the audio and reflecting those emotions in the translated text. This enables translation with natural expressions that include emotions.

[0507] "Speech" refers to human speech and acoustic signals, which, once acquired, can be used as communication data.

[0508] "Acquisition means" refers to a device that collects audio using wireless earphones or similar devices and converts it into a digital signal.

[0509] A "conversion means" refers to a process or device that has the function of analyzing acquired audio and processing it as textual information.

[0510] "Textual information" refers to text data converted from speech, and serves as the basic data for language translation and sentiment analysis.

[0511] "Translation means" refers to the processing or equipment used to convert textual information written in one language into another language.

[0512] "Synthesis means" refers to the process or device for reconstructing translated text information as audio data.

[0513] "Adjustment means" refers to processes that modify the translated text based on information obtained through emotion recognition, thereby reflecting emotions.

[0514] A "transmission means" is a device that has the function of transferring data in order to provide the synthesized audio data to the user's hearing device.

[0515] "Emotion recognition" refers to the technology that analyzes characteristics such as tone, speed, and pitch from speech to identify the speaker's emotional state.

[0516] "Wireless communication" refers to methods of transmitting data to remote locations using technologies such as Bluetooth and Wi-Fi.

[0517] The system of this invention is primarily implemented using wireless earphones, a terminal, and a remote server. First, the user puts on the wireless earphones and inputs voice. This voice is acquired by the earphone's microphone, converted into a digital signal, and transmitted to the terminal. The terminal then transfers the digital voice signal to the server via wireless communication.

[0518] The server converts the acquired audio data into text information using the Google Cloud Speech-to-Text API or similar services. This conversion ensures the audio data is treated as text data. Next, the server translates the converted text data into the specified language using DeepL or other translation services.

[0519] Furthermore, an emotion engine on the server analyzes the audio data to recognize emotions from tone, speed, and pitch. This analysis utilizes technologies such as IBM Watson Tone Analyzer. The recognized emotions are reflected in the translated text, and the translated text is modified by adjustment mechanisms. This process makes the translation more contextually appropriate.

[0520] Finally, the server converts the improved text information back into audio data using tools such as Amazon Polly or Google Cloud Text-to-Speech to generate synthesized speech. This synthesized speech is transmitted to the user's hearing device with low latency, allowing the user to receive it as a natural-sounding translation.

[0521] As a concrete example, if a user were to say "This proposal is groundbreaking" through this system during a business meeting, the system would translate it as "This proposal is groundbreaking" and play it back as an emotionally rich voice.

[0522] An example of a prompt using a generative AI model is, "Design and explain a system that translates speech in real time while considering the user's emotions." This prompt clarifies the user's intent, allowing the system to provide a translation that appropriately reflects those emotions.

[0523] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0524] Step 1:

[0525] The user wears wireless earphones to input voice. The earphone's microphone captures the user's voice and converts this audio signal from analog to digital. The acquired audio data is then transferred to the terminal. The input is the user's voice, and the output is a digital audio signal.

[0526] Step 2:

[0527] The terminal is responsible for transmitting the converted digital audio signal to the server via wireless communication. Specifically, it transfers the audio transmitted to the terminal via Bluetooth communication to the server via the internet using Wi-Fi or 4G / 5G networks. The input for this step is the digital audio signal, and the output is the data to be transferred to the server.

[0528] Step 3:

[0529] The server processes the received digital audio signal and converts it into text information using a speech recognition engine. Specifically, the Google Cloud Speech-to-Text API performs this conversion and outputs the audio data as text data. The input is the audio data sent to the server, and the output is the converted text data.

[0530] Step 4:

[0531] The server uses translation tools to translate the converted text data into the specified language. Translation services such as DeepL are utilized to translate the text data into different languages. The input for this step is text data, and the output is translated text data.

[0532] Step 5:

[0533] The server recognizes emotions from speech. It analyzes the tone, speed, and pitch, which are features of the speech data. IBM Watson Tone Analyzer performs this analysis, identifies the emotional state, and outputs the results. In this step, the input is the initial digital speech signal, and the output is the recognized emotion data.

[0534] Step 6:

[0535] The server adjusts the translated text based on recognized sentiment data. It makes adjustments to reflect the emotional content, correcting it to a natural expression that includes emotion. The input is the translated text data and sentiment data, and the output is the adjusted text data.

[0536] Step 7:

[0537] Finally, the server uses a speech synthesis engine to convert the adjusted text data back into speech data. Amazon Polly or Google Cloud Text-to-Speech are used to generate emotionally responsive synthesized speech. The input is adjusted text data, and the output is synthesized speech.

[0538] Step 8:

[0539] The server transmits the generated synthesized speech to the user with low latency. The transmitted speech is played back through wireless earphones, allowing the user to obtain a natural-sounding translation. The input is synthesized speech, and the output is the speech playback on the user's hearing device.

[0540] (Application Example 2)

[0541] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0542] In communication between people who speak different languages, the challenge lies not only in language barriers but also in the inability to properly convey emotional nuances. In particular, in everyday conversations such as those within the family, there is a need for methods that can accurately convey the speaker's feelings and intentions, rather than simply translating the language. Current translation systems can lead to misunderstandings because they fail to reflect emotions, and therefore need improvement.

[0543] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0544] In this invention, the server includes an acquisition means for acquiring voice data, a conversion means for converting the acquired voice data into encoded data, a language conversion means for translating the converted encoded data into another specified language, and a modification means for adjusting the translated encoded data based on recognized emotion attributes. This enables natural dialogue, including emotions and nuances, between users who speak different languages.

[0545] "Audio data" refers to audio information collected by the acquisition method, and includes the content of the user's speech.

[0546] "Encoded data" refers to data obtained by converting audio data into a different format using various conversion methods. This data forms the basis for translation and emotion recognition.

[0547] A "language conversion means" is an element that has the function of translating encoded data into another specified language.

[0548] "Emotional attributes" refer to information indicating the speaker's emotional state, extracted from the tone and pitch of speech analyzed by recognition tools.

[0549] "Correction measures" refer to functions for adjusting coded data translated based on recognized emotion attributes.

[0550] "Audio signal" refers to sound information sent to auditory devices, generated from encoded data using synthesis methods.

[0551] "User" refers to the person who communicates using the system and receives audio signals through an auditory device.

[0552] The system implementing this invention mainly consists of an auditory device, a terminal, and a remote server. When a user wears the auditory device and speaks, the audio data is transmitted to the server via the terminal.

[0553] On the server, first, the voice acquisition means receives voice data, and the conversion means converts it into encoded data. Next, the language conversion means converts the encoded data into another specified language. At this time, the recognition means analyzes emotional attributes from the tone and pitch of the voice, and the correction means adjusts the translation result based on this information. As a result, the synthesis means generates the final voice signal, which is transmitted to the user's hearing device with low latency.

[0554] This process can be sped up by utilizing cloud computing services. Specifically, it converts audio data into encoded data using Amazon Web Services' speech recognition API, performs language conversion using Google Translate's translation API, and performs emotion recognition using IBM Watson's sentiment analysis service. Finally, it performs speech synthesis using Amazon Polly.

[0555] For example, a robot can act as an intermediary between a Japanese-speaking child and an English-speaking parent within a family, accurately conveying the emotions embedded in the child's Japanese speech into English. The parent's English responses are also processed similarly, enabling smooth, two-way communication.

[0556] The example prompt shows that when a user says, "Can you tell me what happened at school today?", it is translated as "Tell me about what happened at school today?".

[0557] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0558] Step 1:

[0559] When a user speaks, audio data is captured by the microphone of the hearing device. The terminal transmits this audio data to the server using wireless communication. The input is the user's voice, and the output is the transfer of audio data to the server.

[0560] Step 2:

[0561] The server converts the received audio data into encoded data using speech recognition software, such as Amazon Web Services' speech recognition API. This process involves data processing that analyzes the audio frequency and waveform and converts it into text data. The input is audio data, and the output is encoded data.

[0562] Step 3:

[0563] The converted encoded data is translated into the specified language using Google Translate's translation API. The server performs the data conversion between languages ​​through this process. The input is encoded data, and the output is translated encoded data.

[0564] Step 4:

[0565] The server uses IBM Watson's sentiment analysis service to analyze the speaker's sentiment attributes from the translated encoded data. This process analyzes features such as speech tone, pitch, and speed to calculate the sentiment attributes. The input is the translated encoded data, and the output is the identified sentiment attributes.

[0566] Step 5:

[0567] The correction mechanism allows the server to adjust the translation results based on sentiment attributes. Sentiment data is used to fine-tune the translated text data to reflect the desired sentiment. The input consists of sentiment attributes and translated encoded data, while the output is the adjusted encoded data.

[0568] Step 6:

[0569] Using Amazon Polly as the synthesis method, the server converts the tuned encoded data into a speech signal. The synthesized speech reflects emotions, resulting in natural-sounding dialogue. The input is tuned encoded data, and the output is a synthesized speech signal.

[0570] Step 7:

[0571] Ultimately, the server transmits the audio signal to the hearing device with low latency, and the user receives it. It becomes possible to hear the translated audio instantly, allowing for smooth conversations even between speakers of different languages. The input is the synthesized audio signal, and the output is its delivery to the user's hearing device.

[0572] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0573] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0574] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0575] [Fourth Embodiment]

[0576] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0577] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0578] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0579] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0580] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0581] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0582] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0583] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0584] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0585] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0586] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0587] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0588] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0589] This invention relates to a real-time translation system combining wireless earphones and a server. This system uses a speech acquisition means to acquire the user's speech and a text conversion means to convert that speech into text. The converted text is then translated on the server side by a translation means into the language specified by the server.

[0590] The server converts the translated text into audio data using speech synthesis technology and transmits that audio to wireless earphones in real time. By listening to this audio through the earphones, the user can hear what they have said played back in another language, and also hear what others have said in a translated state.

[0591] As a concrete example, when a user speaks Japanese, the wireless earphones capture the speech and transmit it to a server. The server converts the received audio into text, translates it into, for example, English, and then uses speech synthesis to send the English audio data back to the wireless earphones. As a result, even if the user speaks in Japanese, the content will be automatically translated into English and conveyed to the person speaking another language. This makes it possible to provide real-time, seamless communication between different languages.

[0592] In addition, wireless earphones, as terminals, are lightweight and highly portable, eliminating the need for users to carry around bulky traditional translation devices. Furthermore, by utilizing high-performance AI on a server, more accurate translations can be performed, providing users with a more natural conversational experience. This system configuration significantly improves the practicality of real-time translation, and its use is expected to expand to a wide range of situations.

[0593] The following describes the processing flow.

[0594] Step 1:

[0595] The user wears wireless earphones and speaks what they want to say in their own language. The earphone's microphone captures the audio and processes it to send it to the device as digital data.

[0596] Step 2:

[0597] The terminal receives the acquired voice data and transfers it to the server using wireless communication. During this process, the voice data may be compressed or encrypted to minimize latency.

[0598] Step 3:

[0599] The server analyzes the received audio data and converts it into text data using speech recognition technology. This converted text is then used as input data for subsequent translation processes.

[0600] Step 4:

[0601] The server translates text data into the specified target language. By using an AI-based translation engine, it performs context-aware, highly accurate translations.

[0602] Step 5:

[0603] The server converts the translated text into speech data using speech synthesis technology. This speech data is generated in a form that closely resembles natural speech and is in a format that users can easily understand.

[0604] Step 6:

[0605] The server sends audio data to the terminal. During transmission, the audio data is appropriately formatted to maintain sound quality and enable real-time playback.

[0606] Step 7:

[0607] The device receives audio data transmitted from the server and plays it back to the user's ears through the speaker in the wireless earphones. By listening to the translated audio, the user can understand the other person's language and communicate in real time.

[0608] (Example 1)

[0609] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0610] This real-time translation system aims to overcome challenges in facilitating communication between different languages, such as the lack of portability of conventional devices, limitations in translation accuracy, and the disruption of natural conversation due to delays in audio data.

[0611] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0612] In this invention, the server includes means for acquiring audio signals using an acoustic acquisition device and converting them into text data using a character conversion device, means for translating them into another specified language using a translation device, and means for converting them into audio data using an acoustic synthesis device. This makes it possible to provide real-time and natural communication between different languages ​​through a highly portable device.

[0613] An "acoustic acquisition device" is a device that acquires audio signals and converts them into a processable data format.

[0614] A "character conversion device" is a device that analyzes acquired audio signals and converts them into corresponding character data.

[0615] A "translation device" is a device used to convert text data into another specified language.

[0616] A "sound synthesis device" is a device that converts translated text data back into sound data and outputs it in a playable format.

[0617] A "voice transmission device" is a device that provides converted audio data to the user's hearing device.

[0618] "Wireless communication" is a technology that transmits data without using an energy medium, and generally refers to methods that use radio waves.

[0619] "Remote computing resources" refer to computing devices and processing power located in physically distant locations that are accessible via a network.

[0620] This invention is a system for real-time communication between different languages ​​and includes a series of devices for acquiring, translating, and reproducing audio signals. The user speaks using wireless earphones as an audio acquisition device. The earphones acquire the audio signal and transmit this data to a server via a terminal.

[0621] The server converts the received audio data into text data using Google Cloud Speech-to-Text or an equivalent speech recognition API. Next, it translates this text data into the specified language using a generative AI model. OpenAI's GPT is one example of an AI model that can be used here. For translation, a prompt like the following can be used: "Translate the following Japanese into English: 'It's a nice day today.'"

[0622] The translated text data is synthesized into audio data using Amazon Polly or the Google Text-to-Speech API. The server transmits this audio data to the user's wireless earphones with low latency, allowing the user to hear the translated audio. This enables users who speak different languages ​​to communicate naturally.

[0623] Through the process described above, this system provides highly portable, accurate, and real-time translation, enabling users to enjoy comfortable international communication. For example, a user's Japanese utterance, "Ohayou gozaimasu" (Good morning), is transmitted to the other party in real time via the server process as "Good morning" in English. This system supports efficient dialogue in a variety of international situations.

[0624] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0625] Step 1:

[0626] The user speaks through wireless earphones. This audio signal is directly captured by the earphone's microphone. The input is the user's spoken audio signal, and the output is digitized audio data. At this stage, the audio is converted from analog to digital and temporarily stored within the device.

[0627] Step 2:

[0628] The terminal transmits digitized audio data to the server via wireless communication technology (e.g., Bluetooth). The input is the digital audio data stored on the terminal, and the output is the audio data sent to the server. In this process, the data is divided into packets, error checked, and then forwarded to the server.

[0629] Step 3:

[0630] The server processes the received audio data and converts it into text data using a speech recognition API (e.g., acoustic recognition technology). The input is the digital audio data received by the server, and the output is the converted text data. At this stage, the audio is analyzed to identify the exact string from the phonemes in the speech.

[0631] Step 4:

[0632] The server uses a generative AI model to translate acquired text data into a specified language. The input is text data, and the output is translated text data. A prompt "Translate the following text" is sent to the generative AI model, and the translated text is obtained as a response.

[0633] Step 5:

[0634] The server converts the translated text data into speech data using a speech synthesis API (e.g., audio synthesis technology). The input is the translated text data, and the output is the synthesized speech data. At this stage, the speech waveform is synthesized and regenerated as natural speech.

[0635] Step 6:

[0636] The server transmits synthesized audio data to the terminal with low latency using wireless communication technology. The input is the synthesized audio data on the server, and the output is the received audio data. The data necessary for playback is transmitted to the terminal in real time.

[0637] Step 7:

[0638] The device outputs the received audio data to the user's wireless earphones. The input is the received audio data, and the output is the audio the user hears. The user can hear the translated audio in real time and communicate smoothly with others.

[0639] (Application Example 1)

[0640] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0641] There is a need to provide means for people who speak different languages ​​to communicate smoothly. However, existing translation devices are large and inconvenient to carry, making them difficult to use in daily life or while traveling. Furthermore, there is a lack of systems that can provide a smooth and natural conversation experience. To solve these problems, a new translation system is needed.

[0642] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0643] In this invention, the server includes an information acquisition means for acquiring audio data, a character conversion means for converting the acquired audio data into character data, and a translation device for converting the converted character data into another specified language. This enables smooth, real-time communication between people who speak different languages.

[0644] "Information acquisition means" refers to devices or processes that have the function of collecting audio or data.

[0645] A "text conversion means" refers to a device or process that has the function of converting acquired audio data into text information.

[0646] A "translation device" is a device or process that has the function of converting textual information from one language to another.

[0647] "Speech regeneration means" refers to a device or process that has the function of converting text information into speech data and outputting it.

[0648] "Voice supply means" refers to a device or process that has the function of providing converted voice data to a user.

[0649] Wireless communication is a technology that uses radio waves to transfer data without using cables.

[0650] A "processing device" refers to a computer system or server used for data processing.

[0651] "Aircraft" refers to the entire device that is equipped with voice acquisition means and is used to process or output information.

[0652] To implement this invention, it is first necessary to prepare a device for collecting voice as an information acquisition means. The information acquisition means is mounted on a mobile or fixed acoustic device and mainly acquires the user's voice in real time. The acquired voice is transmitted to a processing device using wireless communication technology. Specifically, wireless communication standards such as Bluetooth and Wi-Fi are used here.

[0653] The server first uses speech recognition software such as Google Cloud Speech-to-Text to process the audio, converting the audio data into text data. Next, it is translated into the target language via a translation tool such as the Google Cloud Translation API. Finally, the translated text data is converted back into audio data using a speech regeneration tool such as Amazon Polly.

[0654] The converted audio data is provided to the user through an audio supply means. Specifically, the user can listen to the audio in real time using earphones or speakers connected to their device.

[0655] For example, this invention is extremely useful when a tourist information robot interacts with tourists who speak different languages. When the robot hears a tourist's question, "What are the highlights of this place?", the server translates it to "What are the highlights of this place?" and immediately outputs it as audio, providing the tourist with information in the appropriate language.

[0656] An example of a prompt in a generative AI model is "Please translate the text from the original language to the target language." Using this prompt can yield highly accurate translation output.

[0657] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0658] Step 1:

[0659] The device acquires the user's voice. When the user speaks into the microphone, the voice is captured as an analog signal. This analog signal is converted into digital data using digital signal processing technology and transmitted to the server via wireless communication.

[0660] Step 2:

[0661] The server converts received audio data into text data using speech recognition software. The input is digitized audio data, and the output is the corresponding string of characters. This conversion uses a generative AI model to extract audio features and convert the audio to text based on those features.

[0662] Step 3:

[0663] The server translates text data into the specified language using a translation device. The input is text data in the original language, and the output is the translated text data. The prompt is in the format of "Translate the text in the original language into the target language," and the generative AI model generates the optimal translation result.

[0664] Step 4:

[0665] The server converts the translated text data into audio data using a speech regeneration mechanism. The input is translated text data, and the output is audio data. In this process, natural-sounding speech is generated using speech synthesis technology and represented as sound waves.

[0666] Step 5:

[0667] The server transmits the generated audio data to the terminal using an audio supply device. The terminal receives the transmitted audio data and plays it back to the user through earphones or speakers. This allows the user to listen to the translated audio in real time.

[0668] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0669] This invention is a system that integrates wireless earphones and a remote server to enable a more natural and contextually appropriate translation experience by performing text conversion, translation, speech synthesis, and emotion recognition of voice data. This system improves the quality of dialogue by translating the user's voice in real time and reflecting the user's emotional state in the translation.

[0670] Users wear wireless earphones and can freely speak different languages. Their speech is captured by the earphone's microphone and sent as audio data from the device to the server. The server converts the audio data into text data and then translates the text into the specified language. During this process, the server incorporates an emotion engine that detects the user's emotions from the audio data.

[0671] The emotion engine analyzes features such as tone, speed, and pitch from speech to identify emotional states such as anger, joy, and surprise. This emotion data is fed back into the translation process. For example, if a user is expressing dissatisfaction, the translation result is adjusted to use expressions appropriate to that emotion. By appropriately conveying emotional nuances, misunderstandings are reduced, and communication becomes smoother.

[0672] Furthermore, the server generates emotionally reflective speech using speech synthesis and transmits it to wireless earphones as translated speech. The user can receive this translated speech naturally, enabling smooth, back-and-forth conversation. For example, in a business meeting, when a user speaks enthusiastically about a proposal, the system corrects the translation to ensure that the emotion is accurately conveyed to the other party. In this way, the present invention supports communication that transcends language barriers and provides a smooth conversational environment by accurately conveying emotions.

[0673] The following describes the processing flow.

[0674] Step 1:

[0675] The user uses wireless earphones and begins speaking naturally in their own language. The earphones capture the voice through their built-in microphone, convert it into audio data, and transmit it to the device.

[0676] Step 2:

[0677] The terminal temporarily stores the acquired audio data and transmits it to the server via wireless communication. During this process, the data is compressed as needed for faster processing.

[0678] Step 3:

[0679] The server converts the received audio data into text using a speech recognition engine. This converted text is then used as input data for the next translation step.

[0680] Step 4:

[0681] The server activates an emotion engine to analyze the user's emotions from the voice data. It identifies the user's emotional state based on information such as voice tone, pitch, and speed.

[0682] Step 5:

[0683] When the server translates text data into the specified target language, it adjusts the translation results by taking into account the analyzed sentiment information. This adjustment ensures that the translated text appropriately reflects the user's intent and emotions.

[0684] Step 6:

[0685] The server uses a speech synthesis engine to convert the emotionally charged translated text into speech. The synthesized speech is then adjusted to sound natural while maintaining the original emotion.

[0686] Step 7:

[0687] The server sends the converted audio data back to the device. The device then transfers the received audio data to wireless earphones, allowing the user to listen to it in real time. This enables the user to smoothly share the translated audio with others.

[0688] (Example 2)

[0689] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0690] In communication between users who speak different languages, there is a challenge in accurately conveying not only the sound but also nuances, including emotions. In particular, if text translation remains mechanical and does not reflect the user's emotions, misunderstandings and inefficient communication can occur. Therefore, we want to improve the quality of speech translation and meet the demand for accurately conveying emotions.

[0691] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0692] In this invention, the server includes an acquisition means for acquiring audio, a conversion means for converting the acquired audio into text information, and an adjustment means for recognizing emotions from the audio and reflecting those emotions in the translated text. This enables translation with natural expressions that include emotions.

[0693] "Speech" refers to human speech and acoustic signals, which, once acquired, can be used as communication data.

[0694] "Acquisition means" refers to a device that collects audio using wireless earphones or similar devices and converts it into a digital signal.

[0695] A "conversion means" refers to a process or device that has the function of analyzing acquired audio and processing it as textual information.

[0696] "Textual information" refers to text data converted from speech, and serves as the basic data for language translation and sentiment analysis.

[0697] "Translation means" refers to the processing or equipment used to convert textual information written in one language into another language.

[0698] "Synthesis means" refers to the process or device for reconstructing translated text information as audio data.

[0699] "Adjustment means" refers to processes that modify the translated text based on information obtained through emotion recognition, thereby reflecting emotions.

[0700] A "transmission means" is a device that has the function of transferring data in order to provide the synthesized audio data to the user's hearing device.

[0701] "Emotion recognition" refers to the technology that analyzes characteristics such as tone, speed, and pitch from speech to identify the speaker's emotional state.

[0702] "Wireless communication" refers to methods of transmitting data to remote locations using technologies such as Bluetooth and Wi-Fi.

[0703] The system of this invention is primarily implemented using wireless earphones, a terminal, and a remote server. First, the user puts on the wireless earphones and inputs voice. This voice is acquired by the earphone's microphone, converted into a digital signal, and transmitted to the terminal. The terminal then transfers the digital voice signal to the server via wireless communication.

[0704] The server converts the acquired audio data into text information using the Google Cloud Speech-to-Text API or similar services. This conversion ensures the audio data is treated as text data. Next, the server translates the converted text data into the specified language using DeepL or other translation services.

[0705] Furthermore, an emotion engine on the server analyzes the audio data to recognize emotions from tone, speed, and pitch. This analysis utilizes technologies such as IBM Watson Tone Analyzer. The recognized emotions are reflected in the translated text, and the translated text is modified by adjustment mechanisms. This process makes the translation more contextually appropriate.

[0706] Finally, the server converts the improved text information back into audio data using tools such as Amazon Polly or Google Cloud Text-to-Speech to generate synthesized speech. This synthesized speech is transmitted to the user's hearing device with low latency, allowing the user to receive it as a natural-sounding translation.

[0707] As a concrete example, if a user were to say "This proposal is groundbreaking" through this system during a business meeting, the system would translate it as "This proposal is groundbreaking" and play it back as an emotionally rich voice.

[0708] An example of a prompt using a generative AI model is, "Design and explain a system that translates speech in real time while considering the user's emotions." This prompt clarifies the user's intent, allowing the system to provide a translation that appropriately reflects those emotions.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] Step 1:

[0711] The user inputs voice by wearing wireless earphones. The earphone's microphone captures the user's voice and converts this audio signal from analog to digital. The acquired audio data is transferred to the terminal. The input is the user's voice, and the output is a digital audio signal.

[0712] Step 2:

[0713] The terminal is responsible for transmitting the converted digital audio signal to the server via wireless communication. Specifically, it transfers the audio transmitted to the terminal via Bluetooth communication to the server via the internet using Wi-Fi or 4G / 5G networks. The input for this step is the digital audio signal, and the output is the data to be transferred to the server.

[0714] Step 3:

[0715] The server processes the received digital audio signal and converts it into text information using a speech recognition engine. Specifically, the Google Cloud Speech-to-Text API performs this conversion and outputs the audio data as text data. The input is the audio data sent to the server, and the output is the converted text data.

[0716] Step 4:

[0717] The server uses translation tools to translate the converted text data into the specified language. Translation services such as DeepL are utilized to translate the text data into different languages. The input for this step is text data, and the output is translated text data.

[0718] Step 5:

[0719] The server recognizes emotions from speech. It analyzes the tone, speed, and pitch, which are features of the speech data. IBM Watson Tone Analyzer performs this analysis, identifies the emotional state, and outputs the results. In this step, the input is the initial digital speech signal, and the output is the recognized emotion data.

[0720] Step 6:

[0721] The server adjusts the translated text based on recognized sentiment data. It makes adjustments to reflect the emotional content, correcting it to a natural expression that includes emotion. The input is the translated text data and sentiment data, and the output is the adjusted text data.

[0722] Step 7:

[0723] Finally, the server uses a speech synthesis engine to convert the adjusted text data back into speech data. Amazon Polly or Google Cloud Text-to-Speech are used to generate emotionally responsive synthesized speech. The input is adjusted text data, and the output is synthesized speech.

[0724] Step 8:

[0725] The server transmits the generated synthesized speech to the user with low latency. The transmitted speech is played back through wireless earphones, allowing the user to obtain a natural-sounding translation. The input is synthesized speech, and the output is the speech playback on the user's hearing device.

[0726] (Application Example 2)

[0727] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0728] In communication between people who speak different languages, the challenge lies not only in language barriers but also in the inability to properly convey emotional nuances. In particular, in everyday conversations such as those within the family, there is a need for methods that can accurately convey the speaker's feelings and intentions, rather than simply translating the language. Current translation systems can lead to misunderstandings because they fail to reflect emotions, and therefore need improvement.

[0729] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0730] In this invention, the server includes an acquisition means for acquiring voice data, a conversion means for converting the acquired voice data into encoded data, a language conversion means for translating the converted encoded data into another specified language, and a modification means for adjusting the translated encoded data based on recognized emotion attributes. This enables natural dialogue, including emotions and nuances, between users who speak different languages.

[0731] "Audio data" refers to audio information collected by the acquisition method, and includes the content of the user's speech.

[0732] "Encoded data" refers to data obtained by converting audio data into a different format using various conversion methods. This data forms the basis for translation and emotion recognition.

[0733] A "language conversion means" is an element that has the function of translating encoded data into another specified language.

[0734] "Emotional attributes" refer to information indicating the speaker's emotional state, extracted from the tone and pitch of speech analyzed by recognition tools.

[0735] "Correction measures" refer to functions for adjusting coded data translated based on recognized emotion attributes.

[0736] "Audio signal" refers to sound information sent to auditory devices, generated from encoded data using synthesis methods.

[0737] "User" refers to the person who communicates using the system and receives audio signals through an auditory device.

[0738] The system implementing this invention mainly consists of an auditory device, a terminal, and a remote server. When a user wears the auditory device and speaks, the audio data is transmitted to the server via the terminal.

[0739] On the server, first, the voice acquisition means receives voice data, and the conversion means converts it into encoded data. Next, the language conversion means converts the encoded data into another specified language. At this time, the recognition means analyzes emotional attributes from the tone and pitch of the voice, and the correction means adjusts the translation result based on this information. As a result, the synthesis means generates the final voice signal, which is transmitted to the user's hearing device with low latency.

[0740] This process can be sped up by utilizing cloud computing services. Specifically, it converts audio data into encoded data using Amazon Web Services' speech recognition API, performs language conversion using Google Translate's translation API, and performs emotion recognition using IBM Watson's sentiment analysis service. Finally, it performs speech synthesis using Amazon Polly.

[0741] For example, a robot can act as an intermediary between a Japanese-speaking child and an English-speaking parent within a family, accurately conveying the emotions embedded in the child's Japanese speech into English. The parent's English responses are also processed similarly, enabling smooth, two-way communication.

[0742] The example prompt shows that when a user says, "Can you tell me what happened at school today?", it is translated as "Tell me about what happened at school today?".

[0743] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0744] Step 1:

[0745] When a user speaks, audio data is captured by the microphone of the hearing device. The terminal transmits this audio data to the server using wireless communication. The input is the user's voice, and the output is the transfer of audio data to the server.

[0746] Step 2:

[0747] The server converts the received audio data into encoded data using speech recognition software, such as Amazon Web Services' speech recognition API. This process involves data processing that analyzes the audio frequency and waveform and converts it into text data. The input is audio data, and the output is encoded data.

[0748] Step 3:

[0749] The converted encoded data is translated into the specified language using Google Translate's translation API. The server performs the data conversion between languages ​​through this process. The input is encoded data, and the output is translated encoded data.

[0750] Step 4:

[0751] The server uses IBM Watson's sentiment analysis service to analyze the speaker's sentiment attributes from the translated encoded data. This process analyzes features such as speech tone, pitch, and speed to calculate the sentiment attributes. The input is the translated encoded data, and the output is the identified sentiment attributes.

[0752] Step 5:

[0753] The correction mechanism allows the server to adjust the translation results based on sentiment attributes. Sentiment data is used to fine-tune the translated text data to reflect the desired sentiment. The input consists of sentiment attributes and translated encoded data, while the output is the adjusted encoded data.

[0754] Step 6:

[0755] Using Amazon Polly as the synthesis method, the server converts the tuned encoded data into a speech signal. The synthesized speech reflects emotions, resulting in natural-sounding dialogue. The input is tuned encoded data, and the output is a synthesized speech signal.

[0756] Step 7:

[0757] Ultimately, the server transmits the audio signal to the hearing device with low latency, and the user receives it. It becomes possible to hear the translated audio instantly, allowing for smooth conversations even between speakers of different languages. The input is the synthesized audio signal, and the output is its delivery to the user's hearing device.

[0758] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0759] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0760] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0761] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0762] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0763] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0764] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0765] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0766] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0767] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0768] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0769] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0770] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0771] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0772] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0773] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0774] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0775] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0776] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0777] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0778] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0779] The following is further disclosed regarding the embodiments described above.

[0780] (Claim 1)

[0781] A means for acquiring audio data,

[0782] A text conversion means for converting acquired audio data into text data,

[0783] A translation method that translates converted text data into another specified language,

[0784] A speech synthesis means for converting translated text data into audio data,

[0785] A voice transmission means that provides the converted audio data to the user's hearing device,

[0786] A system that includes this.

[0787] (Claim 2)

[0788] The system according to claim 1, wherein the voice acquisition means transmits voice data to a remote server using wireless communication.

[0789] (Claim 3)

[0790] The system according to claim 1, wherein the audio transmission means is configured to play audio data to the user's hearing device with low latency.

[0791] "Example 1"

[0792] (Claim 1)

[0793] An acoustic acquisition device that acquires audio signals,

[0794] A character conversion device that converts acquired audio signals into character data,

[0795] A translation device that translates converted character data into another specified language,

[0796] A sound synthesis device that converts translated text data into sound data,

[0797] A sound transmission device that provides converted acoustic data to the user's hearing device,

[0798] A processing means that performs processing using remote computing resources,

[0799] A system that includes this.

[0800] (Claim 2)

[0801] The system according to claim 1, wherein the sound acquisition device transmits voice data to a remote computing device using wireless communication.

[0802] (Claim 3)

[0803] The system according to claim 1, wherein the audio transmission device is configured to reproduce acoustic data to the user's hearing device with low latency.

[0804] "Application Example 1"

[0805] (Claim 1)

[0806] Information acquisition means for acquiring audio data,

[0807] A text conversion means for converting acquired audio data into text data,

[0808] A translation device that converts converted character data into another specified language,

[0809] A voice regeneration means for converting translated text data into audio data,

[0810] Audio supply means for transmitting converted audio data to the user's audio device,

[0811] It is possible to output information using a device equipped with a voice acquisition mechanism.

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the voice acquisition means transmits voice data to a remote processing device using wireless communication.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the audio supply means is configured to provide audio data to the user's audio equipment with low latency.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] A means of acquiring sound,

[0820] A conversion means for converting acquired audio into text information,

[0821] A translation means for translating converted text information into another specified language,

[0822] A synthesis means for converting translated text information into audio data,

[0823] A means of recognizing emotions from speech and reflecting those emotions in the translated text,

[0824] A transmission means that provides the converted and adjusted audio data to the user's hearing device,

[0825] A system that includes this.

[0826] (Claim 2)

[0827] The system according to claim 1, wherein the acquisition means transmits voice data to a remote information processing device using wireless communication.

[0828] (Claim 3)

[0829] The system according to claim 1, wherein the transmission means is configured to reproduce audio data to the user's hearing device with low latency.

[0830] "Application example 2 when combining with an emotional engine"

[0831] (Claim 1)

[0832] A means of acquiring audio data,

[0833] A conversion means for converting acquired audio data into encoded data,

[0834] A language conversion means for translating the converted encoded data into another specified language,

[0835] A recognition means for recognizing the emotional attributes of an utterance from translated encoded data,

[0836] Correction means for adjusting coded data translated based on recognized emotion attributes,

[0837] A synthesis means for converting the adjusted encoded data into an audio signal,

[0838] A means for transmitting the converted audio signal to the user's hearing device,

[0839] A system that includes this.

[0840] (Claim 2)

[0841] The system according to claim 1, which transmits voice data to a remote computing device using wireless communication.

[0842] (Claim 3)

[0843] The system according to claim 1, wherein the transmission means is configured to reproduce an audio signal to the user's hearing device with low latency. [Explanation of symbols]

[0844] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for acquiring audio data, A text conversion means for converting acquired audio data into text data, A translation method that translates converted text data into another specified language, A speech synthesis means for converting translated text data into audio data, A voice transmission means that provides the converted audio data to the user's hearing device, A system that includes this.

2. The system according to claim 1, wherein the voice acquisition means transmits voice data to a remote server using wireless communication.

3. The system according to claim 1, wherein the audio transmission means is configured to play audio data to the user's hearing device with low latency.

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