52 results about "Speech applications" patented technology

A neural network audio codec system and related methods are provided. In one example, a method is provided comprising: obtaining speech audio to be encoded; applying the speech audio to an audio encoder that is part of a neural network audio codec system that includes the audio encoder and an audio decoder. The audio encoder and the audio decoder have been trained in an end-to-end manner. The speech audio is encoded with the audio encoder to generate embedding vectors that represent a snapshot of speech audio attributes over successive timeframes of the raw speech audio, and from the embedding vectors, codeword indices are generated to entries in a codebook. The codeword indices are then transmitted or stored for later retrieval and processing by the audio decoder. Further aspects include converting the speech audio to text based on decoding the codeword indices, translating the speech audio from a first language into a second language by mapping codeword indices from a first language to a second language, converting the speech audio prosody by mapping the codeword indices, text generation and discrimination tasks.Yet another aspect includes converting text to speech audio with a different prosody.

A neural network audio codec system and related methods are provided. In one example, a method is provided comprising: obtaining speech audio to be encoded; applying the speech audio to an audio encoder that is part of a neural network audio codec system that includes the audio encoder and an audio decoder. The audio encoder and the audio decoder have been trained in an end-to-end manner. The speech audio is encoded with the audio encoder to generate embedding vectors that represent a snapshot of speech audio attributes over successive timeframes of the raw speech audio, and from the embedding vectors, codeword indices are generated to entries in a codebook. The codeword indices are then transmitted or stored for later retrieval and processing by the audio decoder.

Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and / or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and / or gestures of the user to provide input or control of the system.

Methods and apparatus for voice transformation, authentication, and metadata communication are disclosed. An example apparatus includes interface circuitry, machine readable instructions, and programmable circuitry to identify an enrollment voice associated with the account, a first voice transformation applied to a first voice input to produce the enrollment voice, the first voice transformation to cause the enrollment voice to include first voice-specific features different from second voice-specific features of the first voice input, access a transformed voice associated with a second voice input provided in association with a request to access the account from a user device, the first voice transformation or a second voice transformation to cause the transformed voice to have third voice-specific features different from fourth voice-specific features of the second voice input, and determine whether to provide the user device access to the account based on the first voice-specific features and the third voice-specific features.

Systems and methods for decoding indented speech from neuronal activity in accordance with embodiments of the invention are illustrated. One embodiment includes a neuronal speech system for decoding intended speech from neuronal signals includes a neuronal signal recorder implanted into a user's brain, including a multielectrode array, controller circuitry, and a communication circuitry capable of transmitting data to a neuronal signal decoder, the neuronal signal decoder located externally from the user's body, including a processor, an input / output interface, and a memory, where the memory contains a neuronal speech application that directs the processor to obtain neuronal signal data from the neuronal signal recorder, where the neuronal signal data describes neuronal activity proximal to the implanted neuronal signal recorder, decode speech features from the neuronal signal data using a neuronal decoding model, construct a speech string from the decoded speech features; and output the constructed speech string via an output device.

A method (500) includes receiving a training utterance (106) including a sequence of audio frames (102) and a corresponding ground-truth transcription (320) and truncating a suffix portion from the sequence of audio frames to provide a prefix sequence of the audio frames. The method also includes processing, using a speech recognition model (200), the prefix sequence of audio frames to provide a partial speech recognition hypothesis (180a) and processing, using a prefix embedding multi-head attention layer (170), a sequence of prefix encodings (210) to generate a prefix prompt embedding (175). The method also includes processing, using a LM (160), the partial speech recognition hypothesis conditioned on the prefix prompt embedding to generate a speculated speech recognition (SSR) hypothesis (180b) and determining a training loss (325) based on the SSR hypothesis and the ground-truth transcription. The method also includes fine-tuning parameters of the prefix embedding multi-head attention layer.

To secure anonymity in an interactive interview service and provide a more efficient business tool by performing voice correction in real time and correcting an element such as dialect and intonation.SOLUTION: A system including generative system AI for performing voice correction includes: means for correcting dialect and intonation of a corresponding voice in real time; means for providing interactive interview service securing anonymity for applying the voice to an interview; and means for automatically scheduling a date of an interview by using a brain of GPT.SELECTED DRAWING: Figure 1

The invention relates to the technical field of intelligent voice recognition and analysis, and discloses an intelligent voice recognition and analysis system for offline recording. The system comprises a recording data acquisition module, a feature hierarchical analysis module, a semantic matching module, an intention decision module, a central processing unit and the like. The recording data acquisition module divides an original audio data stream according to a preset voiceprint classification rule; the feature hierarchical analysis module extracts different semantic feature sets; the semantic matching module maps the feature set into an intention classification label; the intention decision module calls a target execution scheme; and the central processing unit checks the parameters to generate a final control instruction. In addition, the system is further provided with a voice resource scheduling module, an instruction conversion module, a voice archiving module and the like which are used for equipment scheduling, instruction conversion and data storage. The system can effectively process offline complex recording data, accurately recognize voice intentions, improve the intelligent level of voice interaction, and is suitable for various offline voice application scenes.

Systems and methods for protecting a voice application communicably coupled to a large language model (LLM) are disclosed herein. An example method is performed by one or more processors of a computing system. The example method may include: receiving an audio transmission over a communications network from a computing device associated with a user of the voice application; providing a request to the LLM based on the audio transmission; receiving a response to the request from the LLM; and performing one or more preemptive actions based on anticipating an anomaly in at least one of the request or the response.

A system and method providing automated voice-based healthcare plan delivery. The system interfaces with the patient through a voice-controlled personal assistant appliance via the internet. Some embodiments are implemented as voice applications or skills within the eco-system of an existing voice-controlled personal assistant device / appliance (hereinafter VCPAD). The system and method allow a clinician to create, digitally input, and modify a healthcare plan for a patient using a simple computer / web interface and without the need of any software development or computer programming skills, and allows a patient to interface with and query that healthcare plan using a largely conventional voice-controlled digital assistant appliance simply and efficiently to extract information about his / her individual healthcare plan in the patient's own language.

Embodiments of the present application provide a group communication method, comprising: listening, by a first terminal device, to one or more speech application channels; the one or more speech application channels being used by a plurality of second terminal devices to send speech application information; in a case where one or more speech application information is received in the one or more speech application channels, determining a target speech device from the second terminal devices that sent the one or more speech application information; sending, to the plurality of second terminal devices, speech confirmation information in a speech confirmation channel; the speech confirmation information indicating the target speech device and a first communication parameter; and receiving, based on the first communication parameter, group service data sent by the target speech device. Embodiments of the present application also provide a group communication apparatus, device, and chip.

A distributed voice applications system includes a voice application rendering agent and at least one voice applications agent that is configured to provide voice applications to an individual user. A management system may control and direct the voice applications rendering agent to create voice applications that are personalized for individual users based on user characteristics, information about the environment in which the voice applications will be performed, prior user interactions and other information. The voice applications agent and components of customized voice applications may be resident on a local user device which includes a voice browser and speech recognition capabilities. The local device, voice applications rendering agent and management system may be interconnected via a communications network.

To provide a system for implementing more convenient and comfort voice operation by integrating functions of voice recognition, voice composition and control by effectively utilizing audio AI in development of smart earphones.SOLUTION: In a data processing system comprising a data processor 12 and a headset type terminal 314, identification processing by an identification processing section 290 of the data processor 12 includes voice recognition means effectively utilizing audio AI, voice composition means, and control means. Voice input is received and voice data are analyzed and converted into text data by the voice recognition means. The text data are received, converted into voice data and outputted by the voice composition means.SELECTED DRAWING: Figure 6

The embodiments of the present application disclose a speech signal enhancement method, device, storage medium and electronic device, which can be applied to the cloud field, artificial intelligence field or speech application field. The above method includes: performing feature extraction processing on the speech signal to obtain speech feature information; inputting the above speech feature information into the target model to extract the gain signal to obtain the gain signal corresponding to the above speech signal; enhancing the above speech signal based on the above gain signal to obtain an enhancement result; wherein the above target model includes at least one speech processing module, and the above speech processing module is composed of three gate loop units, and the above gate loop unit is the only speech signal processing unit used in the above target model. The present application not only reduces the degree of heterogeneity and computing requirements, but also improves the denoising and enhancement effect.

The application discloses an applied far-field voice wake-up method and system, a terminal and a storage medium. The method comprises the following steps: when it is detected that a user issues an application wake-up instruction, obtaining recording data collected by a target device according to the application wake-up instruction, and performing enhanced noise reduction processing on the recording data to obtain target recording data; confirming the type of the target device to obtain device type information, and performing architecture decision evaluation according to the device type information to obtain a target wake-up architecture; performing data processing on the target recording data according to the target wake-up architecture to obtain an application wake-up signal, and waking up a corresponding voice application according to the application wake-up signal. The application can quickly evaluate the best wake-up support structure by introducing a decision module, and realizes the reuse of a wake-up algorithm module by customizing a unified general interface.

The application relates to the technical field of intelligent speech recognition and analysis, and discloses an intelligent speech recognition and analysis system for offline recording. The system comprises a recording data acquisition module, a feature hierarchical analysis module, a semantic matching module, an intention decision module, a central processing unit and the like. The recording data acquisition module divides original audio data streams according to preset voiceprint classification rules; the feature hierarchical analysis module extracts different semantic feature sets; the semantic matching module maps the feature sets into intention classification labels; the intention decision module calls a target execution scheme; and the central processing unit verifies parameters to generate a final control instruction. In addition, the system is also provided with a voice resource scheduling module, an instruction conversion module and a voice archiving module, which are used for device scheduling, instruction conversion and data storage. The system can effectively process offline complex recording data, accurately recognize voice intentions, improve the intelligent level of voice interaction, and is suitable for various offline voice application scenarios.

The present application discloses a method for extending the frequency band of a speech signal, comprising the following steps: determining a source speech and obtaining a signal spectrum of the source speech; obtaining a harmonic signal and an unvoiced noise signal of an extended speech corresponding to the source speech based on the signal spectrum of the source speech; performing speech synthesis processing on the source speech, the signal spectrum of the source speech, and the harmonic signal and unvoiced noise signal of the extended speech corresponding to the source speech, to obtain the extended speech corresponding to the source speech. The application of the technical solution provided by the present application improves the speech quality of the speech signal obtained after the frequency band extension, thereby improving the user's listening experience. The present application also discloses a speech signal frequency band extension device, equipment, storage medium and computer program product, which have corresponding technical effects.

A computer-implemented system for facilitating a text to speech application, comprising a user communications device 104 configured to communicate with a Large Language Model (LLM) or Large Multimodal Model (LMM) API, the user communications device (104) being configured to: in real time, as soon as a text data stream has started to be received, use a speech emphasis handler module to segment the incoming text data into text data segments based on natural speech patterns and / or emphasis, and pass the text data segments, in order, as they are created, to a speech recognition module to transform each said text data segment into representative digital audio data; in real time, as said representative digital audio data is generated, start feeding said digital audio data to an audio buffer and store it therein; and deliver, in real time and in order, said audio segments to an audio playback device.

The present application discloses a method for speech synthesis, which includes the following steps: obtaining a target text, and inputting the target text into a target spectrum generated by an acoustic model; using a neural network vocoder to infer the target spectrum to obtain a predicted speech waveform; performing pop detection on the speech waveform; if pops are detected in the speech waveform, repeating the step of using a neural network vocoder to infer the target spectrum to obtain a predicted speech waveform until there are no pops in the speech waveform; and outputting the speech corresponding to the speech waveform without pops. By applying the technical solution provided by the present application, there can be no pops in the final output speech, so that the final speech is more stable and reliable, and the quality of the synthesized speech is improved. The present application also discloses a speech synthesis device, equipment and storage medium, which have corresponding technical effects.

Aspects of fine-grained, dynamic noise pattern learning for speech applications include a vehicle with a body and cabin. The vehicle contains a processor coupled to a memory. The processor can be configured to embed multimodal data for environmental and vehicle data. The embedded acoustic data can originate from microphone recordings in the cabin. The processor can chain the embeddings to form a latent vector that characterizes the embeddings, thereby estimating a mean and variance of the latent vector using an adaptive time window. Using the mean and variance of the latent vector, the processor can identify a noise type, with noise type identification being fine-grained over the adaptive time window to accurately emulate vehicle noises.

Methods and systems are provided for detecting and processing gestures, expressions (e.g., facial), tone and / or gestures of the user for the purpose of improving the quality and speed of interactions with computer-based systems. Such information may be detected by one or more sensors such as, for example, electromyography (EMG) sensors used to monitor and record electrical activity produced by muscles that are activated. Other sensor types may be used, such as optical, inertial measurement unit (IMU), or other types of bio-sensors. The system may use one or more sensors to detect speech alone or in combination with gestures, expressions (e.g., facial), tone and / or gestures of the user to provide input or control of the system.

A method for processing audio data includes: obtaining audio data, and obtaining a key frame of the audio data by performing keyword detection on the audio data; determining a second speech application based on the key frame, and sending a frame identifier of the key frame to the second speech application; and extracting first audio data from the audio data according to the key frame, and sending the first audio data to the second speech application for speech recognition.

To provide a system for supporting the intellectual training and growth of a child while reducing the labor of a parent.SOLUTION: The voice recognition system includes a means for acquiring voice uttered by a user, a voice recognition means for converting the acquired voice into text data, and a communication means for transmitting the converted text data. A system comprising: language analysis means for extracting a user's intention and emotional state; response generation means for generating an appropriate response based on the extracted intention and emotional state; communication means for receiving the generated response as text data; speech synthesis means for converting the received response text into voice data; and voice changer means for applying character voices to the converted voice data.SELECTED DRAWING: Figure 1

The invention discloses an iterative updating method, device and system for a voice application, a vehicle and a server, and relates to the technical field of voice recognition, the method is applied to a rail transit vehicle, and the method comprises the following steps: after the current voice application is started, obtaining an original voice data stream collected by a vehicle-mounted pickup; performing voice recognition on the original voice data stream by using a voice recognition model of the current voice application to obtain a voice recognition result; generating and storing a model iteration data set according to the voice recognition results and the recognition voice data corresponding to each voice recognition result; sending the model iteration data set to a cloud server; according to the method, the data required for updating the subsequent voice recognition model in the rail transit vehicle can be automatically acquired, so that data support is provided for iterative updating of the voice recognition model, rapid iterative updating of voice application in a rail transit scene is realized, and the engineering landing speed of a rail transit voice intelligent product is increased.

Aspects of fine-grained, dynamic noise pattern learning for voice applications include a vehicle, the vehicle having a body with a cabin. Embedded within the vehicle is a processor coupled to memory. The processor may be configured to embed multimodal data for environment and vehicle data. The embedded acoustic data may be from microphone-captured data in the cabin. The processor may concatenate the embeddings to form a latent vector characterizing the embeddings to thereby estimate a mean and variance of the latent vector using an adaptive time window. The processor may identify a noise type using the mean and variance of the latent vector, the noise type identification being fine-grained via the adaptive time window to accurately emulate vehicle noise.

The application discloses a voice vehicle control full-link test method and device, electronic equipment and a storage medium. The method comprises the following steps: sending simulated voice instructions to a voice application in sequence, obtaining a controller state after the execution of the instructions is completed, judging whether the execution of the voice instructions is successful according to the controller state, and recording the execution time of the voice instructions when the execution of the voice instructions fails; synchronously intercepting a real vehicle CAN bus log and saving the real vehicle CAN bus log as an ASC file; according to the execution time of each voice instruction that fails to execute, whether the voice instruction corresponds to a CAN signal received by a comfort CAN and an information CAN within a set range of the execution time is inquired from the ASC file; and according to whether the CAN signal is received by the comfort CAN and the information CAN, a verification result is output. The voice vehicle control full-link test method and device, electronic equipment and storage medium provided by the application can automatically verify all voice vehicle control functions and output a verification result.

A system for detection of at least one designated wake-up word for at least one speech-enabled application. The system comprises at least one microphone; and at least one computer hardware processor configured to perform: receiving an acoustic signal generated by the at least one microphone at least in part as a result of receiving an utterance spoken by a speaker; obtaining information indicative of the speaker's identity; interpreting the acoustic signal at least in part by determining, using the information indicative of the speaker's identity and automated speech recognition, whether the utterance spoken by the speaker includes the at least one designated wake-up word; and interacting with the speaker based, at least in part, on results of the interpreting.

The present invention discloses a method and system for verifying electric power dispatching voice instructions based on logic verification, which relates to the technical field of voice recognition and comprises the following steps: obtaining pre-emphasized voice instructions and dividing them into frames; obtaining first instruction data, calculating the impact assessment data of the framing according to the first instruction data and a preset information loss factor, and performing data analysis to obtain the optimal framing length; applying a window function to each frame of voice under the optimal framing length, and performing a fast Fourier transform to obtain the energy distribution and balance data of the voice signal at different frequencies, and adjusting the window function length according to the balance data; performing data analysis on the energy distribution of the voice signal at different frequencies through a Mel filter to obtain a Mel spectrum, and performing a logarithmic conversion to obtain a Mel logarithmic spectrum; performing a cepstrum analysis on the Mel logarithmic spectrum to extract voice features; the present application improves the recognition ability of voice instructions by adjusting the frame length and the length of the window function.

Embodiments of the present application disclose an electronic device and a voice wake-up method, receiving voice data input by a user; performing voice processing on the voice data to obtain text information converted from the voice data; intercepting the text information according to a preset length to obtain a candidate text; dynamically matching a custom wake-up word and the candidate text to obtain a minimum edit distance between the custom wake-up word and the candidate text; calculating a confidence level according to the minimum edit distance; if the confidence level is greater than a threshold, waking up a voice application, the voice application being used to respond to a voice instruction input by the user; and if the confidence level is not greater than the threshold, not waking up the voice application. Embodiments of the present application can improve the accuracy of voice recognition and wake-up, avoid incorrect responses and execution of voice control instructions caused by user mis-wake-up of the voice application, reduce mis-wake-up and missed wake-up, improve wake-up rate and wake-up accuracy, and improve voice control function and user voice use experience.

The invention discloses a far-field voice wake-up method and system for an application, a terminal and a storage medium, and the method comprises the steps: obtaining recording data collected by a target device according to an application wake-up instruction when the application wake-up instruction sent by a user is detected, and carrying out the enhanced noise reduction processing of the recording data, and obtaining target recording data; performing type confirmation on the target device to obtain device type information, and performing architecture decision evaluation according to the device type information to obtain a target wake-up architecture; and performing data processing on the target recording data according to the target wake-up architecture to obtain an application wake-up signal, and waking up a corresponding voice application according to the application wake-up signal. The optimal wake-up support structure can be quickly evaluated by introducing the decision module, and the reuse of the wake-up algorithm module is realized by customizing a unified universal interface.