Method for processing speech signal, electronic device for performing said method, and recording medium

Abstract

Disclosed are a method for processing a speech signal and an electronic device for performing said method. The electronic device according to various embodiments comprises: a processor; and a memory that is electrically coupled to the processor and stores instructions executable by the processor, wherein the processor, when the instructions are executed, may: store a recognition result of a first utterance received from a user; receive a second utterance from the user within a set time after receiving the first utterance; determine a first similarity between the recognition result of the first utterance and a recognition result of the second utterance; if the first similarity is a set threshold value or more, determine a parameter for correcting the recognition result of the second utterance; and correct the recognition result of the second utterance on the basis of the parameter.

Description

A voice signal processing method and an electronic device performing the method, and a recording medium

[0001] The following disclosure relates to a voice signal processing method and an electronic device and recording medium for performing the method.

[0002] As voice recognition becomes more advanced, many products and services are adopting voice recognition as an interface, but they are not able to perfectly recognize human voices.

[0003] To improve recognition accuracy, the speaker's intent can be identified and additional information tailored to that intent can be used. Despite identifying the speaker's intent and utilizing additional information, speech recognition sometimes fails to accurately recognize the user's utterance.

[0004] If the user's speech is not recognized accurately, the user will speak in a different way, such as speaking loudly or slowly.

[0005] The above information may be provided as background art to aid in understanding the present disclosure. No claim or determination is made as to whether any of the above-described matters constitute prior art related to the present disclosure.

[0006] An electronic device according to various embodiments may include a processor and a memory electrically coupled to the processor and storing instructions executable by the processor. The processor may store a recognition result of a first utterance received from a user when the instruction is executed. The processor may receive a second utterance from the user within a set time after receiving the first utterance. The processor may determine a first similarity between a recognition result of the first utterance and a recognition result of the second utterance. The processor may determine a parameter for correcting a recognition result of the second utterance when the first similarity is equal to or greater than a set threshold. The processor may correct the recognition result of the second utterance based on the parameter.

[0007] An electronic device according to various embodiments may include a processor and a memory electrically coupled to the processor and storing instructions executable by the processor. The processor may store a first utterance received from a user when the instruction is executed. The processor may receive a second utterance from the user within a set time after receiving the first utterance. The processor may determine a similarity between the first utterance and the second utterance. If the similarity is greater than or equal to a set threshold, the processor may determine a parameter for processing the second utterance. The processor may determine a voice characteristic of the second utterance based on the parameter.

[0008] A voice signal processing method according to various embodiments may include an operation of storing a recognition result of a first utterance received from a user, an operation of receiving a second utterance from the user within a set time after receiving the first utterance, an operation of determining a first similarity between a recognition result of the first utterance and a recognition result of the second utterance, an operation of determining a parameter for correcting a recognition result of the second utterance when the first similarity is equal to or greater than a set threshold value, and an operation of correcting a recognition result of the second utterance based on the parameter.

[0009] A voice signal processing method according to various embodiments may include an operation of storing a first utterance received from a user, an operation of receiving a second utterance from the user within a set time after receiving the first utterance, an operation of determining a similarity between the first utterance and the second utterance, an operation of determining a parameter for processing the second utterance when the similarity is greater than or equal to a set threshold value, and an operation of determining a voice characteristic of the second utterance based on the parameter.

[0010] FIG. 1 is a block diagram of an electronic device within a network environment according to various embodiments.

[0011] FIG. 2 is a block diagram illustrating an integrated intelligence system according to one embodiment.

[0012] FIG. 3 is a diagram showing a form in which relationship information between concepts and actions is stored in a database according to one embodiment.

[0013] FIG. 4 is a diagram illustrating a user terminal displaying a screen for processing voice input received through an intelligent app, according to one embodiment.

[0014] FIG. 5 is a schematic block diagram of an electronic device according to various embodiments.

[0015] Figure 6 is a flowchart of an operation of a voice signal processing method according to various embodiments.

[0016] FIG. 7 is a flowchart illustrating an operation for determining a recognition result of a second utterance by using candidates for the recognition result of the second utterance according to various embodiments.

[0017] Figure 8 is a flowchart of an operation of a voice signal processing method according to various embodiments.

[0018] FIG. 9 is a diagram illustrating a screen in which an electronic device according to various embodiments provides a recognition result of a first utterance and a recognition result of a second utterance.

[0019] FIG. 10 is a diagram illustrating an operation of an electronic device according to various embodiments to correct candidates for recognition results of a second utterance based on parameters.

[0020] FIG. 11 is a diagram illustrating an operation of an electronic device according to various embodiments to correct candidates for recognition results of a second utterance based on parameters.

[0021] Hereinafter, embodiments will be described in detail with reference to the attached drawings. In the description with reference to the attached drawings, identical components are assigned the same reference numerals regardless of the drawing numbers, and redundant descriptions thereof will be omitted.

[0022] In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" may include any one of the items listed together in that phrase, or all possible combinations thereof.

[0023]

[0024] FIG. 1 is a block diagram of an electronic device (101) within a network environment (100) according to various embodiments. Referring to FIG. 1, in the network environment (100), the electronic device (101) may communicate with the electronic device (102) via a first network (198) (e.g., a short-range wireless communication network), or may communicate with at least one of the electronic device (104) or the server (108) via a second network (199) (e.g., a long-range wireless communication network). In one embodiment, the electronic device (101) may communicate with the electronic device (104) via the server (108). According to one embodiment, the electronic device (101) may include a processor (120), a memory (130), an input module (150), an audio output module (155), a display module (160), an audio module (170), a sensor module (176), an interface (177), a connection terminal (178), a haptic module (179), a camera module (180), a power management module (188), a battery (189), a communication module (190), a subscriber identification module (196), or an antenna module (197). In some embodiments, the electronic device (101) may omit at least one of these components (e.g., the connection terminal (178)), or may have one or more other components added. In some embodiments, some of these components (e.g., the sensor module (176), the camera module (180), or the antenna module (197)) may be integrated into one component (e.g., the display module (160)).

[0025] The processor (120) may, for example, execute software (e.g., a program (140)) to control at least one other component (e.g., a hardware or software component) of the electronic device (101) connected to the processor (120) and perform various data processing or operations. According to one embodiment, as at least a part of the data processing or operations, the processor (120) may store commands or data received from other components (e.g., a sensor module (176) or a communication module (190)) in a volatile memory (132), process the commands or data stored in the volatile memory (132), and store result data in a non-volatile memory (134). According to one embodiment, the processor (120) may include a main processor (121) (e.g., a central processing unit or an application processor) or an auxiliary processor (123) (e.g., a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor) that can operate independently or together with the main processor (121). For example, when the electronic device (101) includes the main processor (121) and the auxiliary processor (123), the auxiliary processor (123) may be configured to use less power than the main processor (121) or to be specialized for a given function. The auxiliary processor (123) may be implemented separately from the main processor (121) or as a part thereof.

[0026] The auxiliary processor (123) may control at least a portion of functions or states associated with at least one component (e.g., a display module (160), a sensor module (176), or a communication module (190)) of the electronic device (101), for example, on behalf of the main processor (121) while the main processor (121) is in an inactive (e.g., sleep) state, or together with the main processor (121) while the main processor (121) is in an active (e.g., application execution) state. In one embodiment, the auxiliary processor (123) (e.g., an image signal processor or a communication processor) may be implemented as a part of another functionally related component (e.g., a camera module (180) or a communication module (190)). In one embodiment, the auxiliary processor (123) (e.g., a neural network processing unit) may include a hardware structure specialized for processing artificial intelligence models. The artificial intelligence models may be generated through machine learning. This learning can be performed, for example, on the electronic device (101) itself where the artificial intelligence model is executed, or can be performed through a separate server (e.g., server (108)). The learning algorithm can include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but is not limited to the examples described above. The artificial intelligence model can include multiple artificial neural network layers.The artificial neural network may be one of a deep neural network (DNN), a convolutional neural network (CNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), a deep Q-network, or a combination of two or more of the above, but is not limited to the examples described above. In addition to, or alternatively to, a hardware structure, an artificial intelligence model may include a software structure.

[0027] The memory (130) can store various data used by at least one component (e.g., processor (120) or sensor module (176)) of the electronic device (101). The data can include, for example, software (e.g., program (140)) and input data or output data for commands related thereto. The memory (130) can include volatile memory (132) or non-volatile memory (134).

[0028] The program (140) may be stored as software in the memory (130) and may include, for example, an operating system (142), middleware (144), or an application (146).

[0029] The input module (150) can receive commands or data to be used in a component of the electronic device (101) (e.g., a processor (120)) from an external source (e.g., a user) of the electronic device (101). The input module (150) can include, for example, a microphone, a mouse, a keyboard, a key (e.g., a button), or a digital pen (e.g., a stylus pen).

[0030] The audio output module (155) can output audio signals to the outside of the electronic device (101). The audio output module (155) can include, for example, a speaker or a receiver. The speaker can be used for general purposes, such as multimedia playback or recording playback. The receiver can be used to receive incoming calls. In one embodiment, the receiver can be implemented separately from the speaker or as part of the speaker.

[0031] The display module (160) can visually provide information to an external party (e.g., a user) of the electronic device (101). The display module (160) may include, for example, a display, a holographic device, or a projector and a control circuit for controlling the device. According to one embodiment, the display module (160) may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of a force generated by the touch.

[0032] The audio module (170) can convert sound into an electrical signal, or vice versa, convert an electrical signal into sound. According to one embodiment, the audio module (170) can acquire sound through the input module (150), output sound through the sound output module (155), or an external electronic device (e.g., electronic device (102)) (e.g., speaker or headphone) directly or wirelessly connected to the electronic device (101).

[0033] The sensor module (176) can detect the operating status (e.g., power or temperature) of the electronic device (101) or the external environmental status (e.g., user status) and generate an electrical signal or data value corresponding to the detected status. According to one embodiment, the sensor module (176) can include, for example, a gesture sensor, a gyro sensor, a barometric pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, a temperature sensor, a humidity sensor, or an illuminance sensor.

[0034] The interface (177) may support one or more designated protocols that may be used to directly or wirelessly connect the electronic device (101) with an external electronic device (e.g., the electronic device (102)). In one embodiment, the interface (177) may include, for example, a high definition multimedia interface (HDMI), a universal serial bus (USB) interface, an SD card interface, or an audio interface.

[0035] The connection terminal (178) may include a connector through which the electronic device (101) may be physically connected to an external electronic device (e.g., electronic device (102)). According to one embodiment, the connection terminal (178) may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (e.g., a headphone connector).

[0036] The haptic module (179) can convert electrical signals into mechanical stimuli (e.g., vibration or movement) or electrical stimuli that a user can perceive through tactile or kinesthetic sensations. According to one embodiment, the haptic module (179) can include, for example, a motor, a piezoelectric element, or an electrical stimulation device.

[0037] The camera module (180) can capture still images and videos. According to one embodiment, the camera module (180) may include one or more lenses, image sensors, image signal processors, or flashes.

[0038] The power management module (188) can manage power supplied to the electronic device (101). According to one embodiment, the power management module (188) can be implemented as, for example, at least a part of a power management integrated circuit (PMIC).

[0039] A battery (189) may power at least one component of the electronic device (101). In one embodiment, the battery (189) may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.

[0040] The communication module (190) may support the establishment of a direct (e.g., wired) communication channel or a wireless communication channel between the electronic device (101) and an external electronic device (e.g., electronic device (102), electronic device (104), or server (108)), and the performance of communication through the established communication channel. The communication module (190) may operate independently from the processor (120) (e.g., application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication. According to one embodiment, the communication module (190) may include a wireless communication module (192) (e.g., a cellular communication module, a short-range wireless communication module, or a global navigation satellite system (GNSS) communication module) or a wired communication module (194) (e.g., a local area network (LAN) communication module, or a power line communication module). Among these communication modules, the corresponding communication module can communicate with an external electronic device (104) via a first network (198) (e.g., a short-range communication network such as Bluetooth, wireless fidelity (WiFi) direct, or infrared data association (IrDA)) or a second network (199) (e.g., a long-range communication network such as a legacy cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., a LAN or WAN)). These various types of communication modules can be integrated into a single component (e.g., a single chip) or implemented as multiple separate components (e.g., multiple chips). The wireless communication module (192) can verify or authenticate the electronic device (101) within a communication network such as the first network (198) or the second network (199) by using subscriber information (e.g., an international mobile subscriber identity (IMSI)) stored in the subscriber identification module (196).

[0041] The wireless communication module (192) can support 5G networks and next-generation communication technologies following the 4G network, such as NR access technology (new radio access technology). The NR access technology can support high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and connection of multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low-latency communications)). The wireless communication module (192) can support, for example, a high-frequency band (e.g., mmWave band) to achieve a high data transmission rate. The wireless communication module (192) can support various technologies for securing performance in a high-frequency band, such as beamforming, massive multiple-input and multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, analog beam-forming, or large scale antenna. The wireless communication module (192) can support various requirements specified in the electronic device (101), an external electronic device (e.g., the electronic device (104)), or a network system (e.g., the second network (199)). According to one embodiment, the wireless communication module (192) can support a peak data rate (e.g., 20 Gbps or more) for eMBB realization, a loss coverage (e.g., 164 dB or less) for mMTC realization, or a U-plane latency (e.g., 0.5 ms or less for downlink (DL) and uplink (UL), or 1 ms or less for round trip) for URLLC realization.

[0042] The antenna module (197) can transmit or receive signals or power to or from an external device (e.g., an external electronic device). In one embodiment, the antenna module (197) may include an antenna including a radiator formed of a conductor or a conductive pattern formed on a substrate (e.g., a PCB). In one embodiment, the antenna module (197) may include a plurality of antennas (e.g., an array antenna). In this case, at least one antenna suitable for a communication method used in a communication network, such as the first network (198) or the second network (199), may be selected from the plurality of antennas, for example, by the communication module (190). A signal or power may be transmitted or received between the communication module (190) and an external electronic device via the at least one selected antenna. In some embodiments, in addition to the radiator, another component (e.g., a radio frequency integrated circuit (RFIC)) may be additionally formed as a part of the antenna module (197).

[0043] According to various embodiments, the antenna module (197) may form a mmWave antenna module. In one embodiment, the mmWave antenna module may include a printed circuit board, an RFIC disposed on or adjacent a first side (e.g., a bottom side) of the printed circuit board and capable of supporting a designated high-frequency band (e.g., a mmWave band), and a plurality of antennas (e.g., an array antenna) disposed on or adjacent a second side (e.g., a top side or a side side) of the printed circuit board and capable of transmitting or receiving signals in the designated high-frequency band.

[0044] At least some of the above components can be interconnected and exchange signals (e.g., commands or data) with each other via a communication method between peripheral devices (e.g., a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0045] According to one embodiment, commands or data may be transmitted or received between the electronic device (101) and an external electronic device (104) via a server (108) connected to a second network (199). Each of the external electronic devices (102 or 104) may be the same or a different type of device as the electronic device (101). According to one embodiment, all or part of the operations executed in the electronic device (101) may be executed in one or more of the external electronic devices (102, 104, or 108). For example, when the electronic device (101) is to perform a certain function or service automatically or in response to a request from a user or another device, the electronic device (101) may, instead of or in addition to executing the function or service itself, request one or more external electronic devices to perform the function or at least a part of the service. One or more external electronic devices that receive the request may execute at least a portion of the requested function or service, or an additional function or service related to the request, and transmit the result of the execution to the electronic device (101). The electronic device (101) may process the result as is or additionally and provide it as at least a portion of a response to the request. For this purpose, cloud computing, distributed computing, mobile edge computing (MEC), or client-server computing technology may be used, for example. The electronic device (101) may provide an ultra-low latency service by using distributed computing or mobile edge computing, for example. In another embodiment, the external electronic device (104) may include an Internet of Things (IoT) device. The server (108) may be an intelligent server utilizing machine learning and / or a neural network. According to one embodiment, the external electronic device (104) or the server (108) may be included in the second network (199).The electronic device (101) can be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.

[0046]

[0047] FIG. 2 is a block diagram illustrating an integrated intelligence system according to one embodiment.

[0048] Referring to FIG. 2, an integrated intelligent system of one embodiment may include an electronic device (101), an intelligent server (200) (e.g., server (108) of FIG. 1), and a service server (300) (e.g., server (108) of FIG. 1).

[0049] The electronic device (101) of one embodiment may be a terminal device (or electronic device) that can connect to the Internet, and may be, for example, a mobile phone, a smart phone, a personal digital assistant (PDA), a laptop computer, a TV, white goods, a wearable device, an HMD, or a smart speaker.

[0050] According to the illustrated embodiment, the electronic device (101) may include an interface (177), an input module (150), an audio output module (155), a display module (160), a memory (130), or a processor (120). The above-listed components may be operatively or electrically connected to each other.

[0051] An interface (177) of one embodiment may be configured to connect to an external device and transmit and receive data. An input module (150) of one embodiment may receive sound (e.g., user speech) and convert it into an electrical signal. An audio output module (155) of one embodiment may output the electrical signal as sound (e.g., voice).

[0052] The display module (160) of one embodiment may be configured to display an image or video. The display module (160) of one embodiment may also display a graphical user interface (GUI) of a running app (or application program). The display module (160) of one embodiment may receive touch input via a touch sensor. For example, the display module (160) may receive text input via a touch sensor in an on-screen keyboard area displayed within the display module (160).

[0053] In one embodiment, the memory (130) may store a client module (151), a software development kit (SDK) (153), and a plurality of apps (146). The client module (151) and the SDK (153) may constitute a framework (or solution program) for performing general functions. In addition, the client module (151) or the SDK (153) may constitute a framework for processing user input (e.g., voice input, text input, touch input).

[0054] The plurality of apps (146) stored in the memory (130) of one embodiment may be programs for performing a specified function. According to one embodiment, the plurality of apps (146) may include a first app (146-1) and a second app (146-2). According to one embodiment, each of the plurality of apps (146) may include a plurality of operations for performing a specified function. For example, the apps may include an alarm app, a message app, and / or a schedule app. According to one embodiment, the plurality of apps (146) may be executed by the processor (120) to sequentially execute at least some of the plurality of operations.

[0055] The processor (120) of one embodiment can control the overall operation of the electronic device (101). For example, the processor (120) can be electrically connected to an interface (177), an input module (150), an audio output module (155), and a display module (160) to perform a specified operation.

[0056] The processor (120) of one embodiment may also execute a program stored in the memory (130) to perform a designated function. For example, the processor (120) may execute at least one of the client module (151) or the SDK (153) to perform the following operations for processing user input. The processor (120) may control the operations of multiple apps (146), for example, through the SDK (153). The following operations described as operations of the client module (151) or the SDK (153) may be operations executed by the processor (120).

[0057] The client module (151) of one embodiment can receive user input. For example, the client module (151) can receive a voice signal corresponding to a user utterance detected through the input module (150). Alternatively, the client module (151) can receive a touch input detected through the display module (160). Alternatively, the client module (151) can receive a text input detected through a keyboard or a visual keyboard. In addition, the client module (151) can receive various forms of user input detected through an input module included in the electronic device (101) or an input module connected to the electronic device (101). The client module (151) can transmit the received user input to the intelligent server (200). The client module (151) can transmit status information of the electronic device (101) to the intelligent server (200) together with the received user input. The status information can be, for example, execution status information of an app.

[0058] The client module (151) of one embodiment can receive a result corresponding to the received user input. For example, the client module (151) can receive a result corresponding to the received voice input when the intelligent server (200) can produce a result corresponding to the received user input. The client module (151) can display the received result on the display module (160). In addition, the client module (151) can output the received result as audio through the audio output module (155).

[0059] In one embodiment, the client module (151) can receive a plan corresponding to the received user input. The client module (151) can display the results of executing multiple operations of the app according to the plan on the display module (160). For example, the client module (151) can sequentially display the results of executing multiple operations on the display and output audio through the audio output module (155). The electronic device (101) can, for example, display only some results of executing multiple operations (e.g., the result of the last operation) on the display module (160) and output audio through the audio output module (155).

[0060] According to one embodiment, the client module (151) may receive a request from the intelligent server (200) to obtain information necessary to produce a result corresponding to a user input. According to one embodiment, the client module (151) may transmit the necessary information to the intelligent server (200) in response to the request.

[0061] The client module (151) of one embodiment can transmit result information of executing multiple operations according to a plan to the intelligent server (200). The intelligent server (200) can use the result information to confirm that the received user input has been processed correctly.

[0062] The client module (151) of one embodiment may include a voice recognition module. According to one embodiment, the client module (151) may recognize voice inputs that perform limited functions through the voice recognition module. For example, the client module (151) may execute an intelligent app to process voice inputs to perform organic actions through designated inputs (e.g., "Wake up!").

[0063] An intelligent server (200) of one embodiment can receive information related to a user voice input from an electronic device (101) via a communication network. According to one embodiment, the intelligent server (200) can change data related to the received voice input into text data. According to one embodiment, the intelligent server (200) can generate a plan for performing a task corresponding to the user voice input based on the text data.

[0064] In one embodiment, the plan may be generated by an artificial intelligence (AI) system. The AI ​​system may be a rule-based system, a neural network-based system (e.g., a feedforward neural network (FNN) or a recurrent neural network (RNN)), or a combination of the above or another AI system. In one embodiment, the plan may be selected from a set of predefined plans or may be generated in real time in response to a user request. For example, the AI ​​system may select at least one plan from a plurality of predefined plans.

[0065] An intelligent server (200) of one embodiment may transmit a result according to a generated plan to an electronic device (101), or transmit the generated plan to the electronic device (101). According to one embodiment, the electronic device (101) may display a result according to the plan on a display module (160). According to one embodiment, the electronic device (101) may display a result of executing an operation according to the plan on a display module (160).

[0066] An intelligent server (200) of one embodiment may include a front end (210), a natural language platform (220), a capsule database (230), an execution engine (240), an end user interface (250), a management platform (260), a big data platform (270), or an analytic platform (280).

[0067] The front end (210) of one embodiment can receive user input from the electronic device (101). The front end (210) can transmit a response corresponding to the user input.

[0068] According to one embodiment, the natural language platform (220) may include an automatic speech recognition module (ASR module) (221), a natural language understanding module (NLU module) (223), a planner module (225), a natural language generator module (NLG module) (227), or a text to speech module (TTS module) (229).

[0069] The automatic speech recognition module (221) of one embodiment can convert voice input received from the electronic device (101) into text data. The natural language understanding module (223) of one embodiment can identify the user's intention using the text data of the voice input. For example, the natural language understanding module (223) can identify the user's intention by performing syntactic analysis or semantic analysis on user input in the form of text data. The natural language understanding module (223) of one embodiment can identify the meaning of words extracted from the user input using linguistic features (e.g., grammatical elements) of morphemes or phrases, and can determine the user's intention by matching the meaning of the identified words to the intent. The natural language understanding module (223) can obtain intent information corresponding to the user's utterance. The intent information may be information indicating the user's intent determined by interpreting text data. The intent information may include information indicating an action or function that the user intends to execute using the device.

[0070] The planner module (225) of one embodiment can generate a plan using the intent and parameters determined by the natural language understanding module (223). According to one embodiment, the planner module (225) can determine a plurality of domains necessary to perform a task based on the determined intent. The planner module (225) can determine a plurality of operations included in each of the plurality of domains determined based on the intent. According to one embodiment, the planner module (225) can determine parameters necessary to execute the determined plurality of operations or result values ​​output by the execution of the plurality of operations. The parameters and the result values ​​can be defined as concepts of a specified format (or class). Accordingly, the plan can include a plurality of operations and a plurality of concepts determined by the user's intent. The planner module (225) can determine the relationship between the plurality of operations and the plurality of concepts in a stepwise (or hierarchical) manner. For example, the planner module (225) can determine the execution order of a plurality of actions based on the user's intention based on a plurality of concepts. In other words, the planner module (225) can determine the execution order of a plurality of actions based on parameters required for the execution of the plurality of actions and results output by the execution of the plurality of actions. Accordingly, the planner module (225) can generate a plan including association information (e.g., ontology) between the plurality of actions and the plurality of concepts. The planner module (225) can generate the plan using information stored in a capsule database (230) in which a set of relationships between concepts and actions is stored.

[0071] The natural language generation module (227) of one embodiment can convert specified information into text format. The information converted into text format may be in the form of natural language utterances. The text-to-speech conversion module (229) of one embodiment can convert information in text format into information in speech format.

[0072] According to one embodiment, some or all of the functions of the natural language platform (220) may also be implemented in the electronic device (101).

[0073] The capsule database (230) may store information on the relationships between a plurality of concepts and actions corresponding to a plurality of domains. According to one embodiment, a capsule may include a plurality of action objects (or action information) and concept objects (or concept information) included in a plan. According to one embodiment, the capsule database (230) may store a plurality of capsules in the form of a concept action network (CAN). According to one embodiment, the plurality of capsules may be stored in a function registry included in the capsule database (230).

[0074] The capsule database (230) may include a strategy registry that stores strategy information required when determining a plan corresponding to a voice input. The strategy information may include reference information for determining a single plan when there are multiple plans corresponding to a user input. According to one embodiment, the capsule database (230) may include a follow-up registry that stores information on follow-up actions for suggesting follow-up actions to a user in a given situation. The follow-up actions may include, for example, follow-up utterances. According to one embodiment, the capsule database (230) may include a layout registry that stores layout information of information output through the electronic device (101). According to one embodiment, the capsule database (230) may include a vocabulary registry that stores vocabulary information included in capsule information. According to one embodiment, the capsule database (230) may include a dialog registry that stores information on dialogue (or interaction) with a user. The capsule database (230) may update stored objects through a developer tool. The developer tool may include, for example, a function editor for updating action objects or concept objects. The developer tool may include a vocabulary editor for updating vocabulary. The developer tool may include a strategy editor for creating and registering strategies that determine plans. The developer tool may include a dialog editor for creating dialogues with the user.The developer tool may include a follow-up editor that activates follow-up goals and allows editing of follow-up utterances that provide hints. The follow-up goals may be determined based on currently set goals, user preferences, or environmental conditions. In one embodiment, the capsule database (230) may also be implemented within the electronic device (101).

[0075] The execution engine (240) of one embodiment can produce a result using the generated plan. The end user interface (250) can transmit the produced result to the electronic device (101). Accordingly, the electronic device (101) can receive the result and provide the received result to the user. The management platform (260) of one embodiment can manage information used in the intelligent server (200). The big data platform (270) of one embodiment can collect user data. The analysis platform (280) of one embodiment can manage the quality of service (QoS) of the intelligent server (200). For example, the analysis platform (280) can manage the components and processing speed (or efficiency) of the intelligent server (200).

[0076] A service server (300) of one embodiment may include CP service A (301), CP service B (302), and CP service C. The service server (300) of one embodiment may provide a service (e.g., food ordering or hotel reservation) specified for an electronic device (101). According to one embodiment, the service server (300) may be a server operated by a third party. The service server (300) of one embodiment may provide information for generating a plan corresponding to a received user input to the intelligent server (200). The provided information may be stored in the capsule database (230). In addition, the service server (300) may provide result information according to the plan to the intelligent server (200).

[0077] In the integrated intelligence system (10) described above, the electronic device (101) can provide various intelligent services to the user in response to user input. The user input may include, for example, input via a physical button, touch input, or voice input.

[0078] In one embodiment, the electronic device (101) may provide a voice recognition service through an intelligent app (or voice recognition app) stored internally. In this case, for example, the electronic device (101) may recognize a user utterance or voice input received through the microphone and provide the user with a service corresponding to the recognized voice input.

[0079] In one embodiment, the electronic device (101) may perform a designated operation, either alone or in conjunction with the intelligent server and / or service server, based on the received voice input. For example, the electronic device (101) may execute an app corresponding to the received voice input and perform a designated operation through the executed app.

[0080] In one embodiment, when an electronic device (101) provides a service together with an intelligent server (200) and / or a service server, the user terminal may detect a user utterance using the input module (150) and generate a signal (or voice data) corresponding to the detected user utterance. The user terminal may transmit the voice data to the intelligent server (200) using the interface (177).

[0081] According to one embodiment, an intelligent server (200) may generate a plan for performing a task corresponding to a voice input received from an electronic device (101), or a result of performing an operation according to the plan. The plan may include, for example, a plurality of operations for performing a task corresponding to a user's voice input, and a plurality of concepts related to the plurality of operations. The concept may define parameters input to the execution of the plurality of operations, or result values ​​output by the execution of the plurality of operations. The plan may include association information between the plurality of operations and the plurality of concepts.

[0082] An electronic device (101) of one embodiment can receive the response using an interface (177). The electronic device (101) can output a voice signal generated within the electronic device (101) to the outside using the audio output module (155), or can output an image generated within the electronic device (101) to the outside using the display module (160).

[0083]

[0084] FIG. 3 is a diagram showing a form in which relationship information between concepts and actions is stored in a database according to various embodiments.

[0085] The capsule database (e.g., the capsule database (230) of FIG. 2) of the intelligent server (e.g., the intelligent server (200) of FIG. 2) may store capsules in the form of a CAN (concept action network). The capsule database may store operations for processing tasks corresponding to a user's voice input and parameters necessary for the operations in the form of a CAN (concept action network).

[0086] The capsule database may store a plurality of capsules (capsule (A) (401), capsule (B) (404)) corresponding to each of a plurality of domains (e.g., applications). According to one embodiment, one capsule (e.g., capsule (A) (401)) may correspond to one domain (e.g., location (geo), application). In addition, one capsule may correspond to at least one service provider (e.g., CP 1 (402), CP 2 (403), CP 3 (406), or CP 4 (405)) for performing a function for a domain related to the capsule. According to one embodiment, one capsule may include at least one operation (410) and at least one concept (420) for performing a specified function.

[0087] The above natural language platform (e.g., the natural language platform (220) of FIG. 2) can generate a plan for performing a task corresponding to a received voice input using capsules stored in a capsule database. For example, the planner module of the natural language platform (e.g., the planner module (225) of FIG. 2) can generate a plan using capsules stored in a capsule database. For example, a plan (407) can be generated using actions (4011, 4013) and concepts (4012, 4014) of capsule A (410) and actions (4041) and concepts (4042) of capsule B (404).

[0088]

[0089] FIG. 4 is a diagram showing a screen for processing voice input received through an intelligent app by a user terminal according to various embodiments.

[0090] The electronic device (101) can execute an intelligent app to process user input through an intelligent server (e.g., the intelligent server (200) of FIG. 2).

[0091] According to one embodiment, on screen 310, when the electronic device (101) recognizes a designated voice input (e.g., wake up!) or receives an input via a hardware key (e.g., a dedicated hardware key), the electronic device (101) may execute an intelligent app for processing the voice input. For example, the electronic device (101) may execute an intelligent app while executing a schedule app. According to one embodiment, the electronic device (101) may display an object (e.g., an icon) (311) corresponding to the intelligent app on the display module (160). According to one embodiment, the electronic device (101) may receive a voice input by a user's speech. For example, the electronic device (101) may receive a voice input such as "Tell me my schedule for this week!" According to one embodiment, the electronic device (101) can display a user interface (UI) (313) (e.g., an input window) of an intelligent app displaying text data of received voice input on a display module (e.g., the display module (160) of FIG. 2).

[0092] According to one embodiment, on the 320 screen, the electronic device (101) can display a result corresponding to the received voice input on the display module (160). For example, the electronic device (101) can receive a plan corresponding to the received user input and display 'this week's schedule' on the display module (160) according to the plan.

[0093]

[0094] FIG. 5 is a schematic block diagram of an electronic device (101) according to various embodiments.

[0095] Referring to FIG. 5, an electronic device (101) according to various embodiments may include a voice receiving unit (510), a voice signal processing unit (520), a voice recognition unit (530), a recognition result post-processing unit (540), a recognition result output unit (550), and a repeated utterance determination unit (560).

[0096] As illustrated in the embodiment of FIG. 5, the electronic device (101) may include, but is not limited to, a voice receiving unit (510), a voice signal processing unit (520), a voice recognition unit (530), a recognition result post-processing unit (540), a recognition result output unit (550), and a repetition utterance determining unit (560), each of which includes processing circuitry (e.g., the processor (120) of FIG. 1) and / or memory (e.g., the memory (130) of FIG. 1). For example, the electronic device (101) may perform substantially the same functions and / or operations of the voice receiving unit (510), the voice signal processing unit (520), the voice recognition unit (530), the recognition result post-processing unit (540), the recognition result output unit (550), and the repetition utterance determining unit (560) illustrated in FIG. 5 using the processor (120) and / or the memory (130).

[0097] The electronic device (101) can receive a first utterance and a second utterance from the user through the voice receiving unit (510) (e.g., the input module (150) or microphone of FIG. 1). For example, the second utterance can represent an utterance received from the user within a set time after the first utterance is received, after the recognition result of the first utterance is provided, or after an action is performed based on the recognition result of the first utterance.

[0098] For example, the electronic device (101) can determine who the user of the received utterance is. If the user of the first utterance and the second utterance are the same, the electronic device (101) can determine whether the second utterance is a repetitive utterance.

[0099] The received first utterance and / or second utterance may be stored in an audio buffer.

[0100] The electronic device (101) can process the first utterance and / or the second utterance stored in the audio buffer using the voice signal processing unit (520). The voice signal processing unit (520) can process the first utterance and / or the second utterance using parameters related to voice signal processing, and determine voice features corresponding to the first utterance and voice features corresponding to the second utterance.

[0101] The electronic device (101) can determine the recognition result of the first utterance and / or the recognition result of the second utterance using the voice recognition unit (530). Hereinafter, when the second utterance is determined to be a repetitive utterance, the operation of correcting and / or determining the recognition result of the second utterance will be described. However, the electronic device (101) can apply the operation of correcting and / or determining the recognition result of the second utterance substantially in the same manner as the first utterance.

[0102] For example, the electronic device (101) can correct the recognition result of the first utterance and / or the recognition result of the second utterance using the recognition result post-processing unit (540). For example, the recognition result post-processing unit (540) can correct the recognition result of the first utterance and / or the recognition result of the second utterance based on parameters for correcting the recognition result of the first utterance and / or the recognition result of the second utterance.

[0103] For example, the electronic device (101) may output the recognition result of the corrected first utterance and / or the recognition result of the second utterance using the recognition result output unit (550). For example, the electronic device (101) may provide the recognition result of the corrected first utterance and / or the recognition result of the second utterance to the user. For example, the electronic device (101) may provide the recognition result through a display module (e.g., the display module (160) of FIG. 1).

[0104] The electronic device (101) can provide the recognition result of the corrected first utterance and / or the recognition result of the second utterance to another device, server and / or service.

[0105] For example, the electronic device (101) can use the repeated utterance determination unit (560) to determine the similarity between the first utterance and the second utterance. For example, the electronic device (101) can use the repeated utterance determination unit (560) to determine the first similarity between the recognition result of the first utterance and the recognition result of the second utterance.

[0106] For example, the electronic device (101) can determine the similarity between the first utterance and the second utterance based on the voice signal information of the first utterance and the voice signal information of the second utterance. For example, the voice signal information can include the energy of the signal, the speed, the noise level, and the end point of the voice section.

[0107] For example, the voice signal information of the first utterance may be stored in the memory (130) (e.g., cache) (for a set time period after receiving the first utterance). If the second utterance is received within the set time period after receiving the first utterance, the electronic device (101) may determine the similarity between the voice signal information of the first utterance stored in the memory (130) and the voice signal information of the second utterance.

[0108] For example, the electronic device (101) can determine the similarity between the voice signal information of the first utterance and the voice signal information of the second utterance from the perspective of signal similarity. For example, the electronic device (101) can determine the similarity using the cross-correlation and spectrogram of the voice signal information of the first utterance and the voice signal information of the second utterance. The electronic device (101) can determine the similarity using various information of the voice signal information of the first and second utterances, and the parameters for determining the similarity are not limited to the examples described above.

[0109] For example, if the similarity between the voice signal information of the first utterance and the voice signal information of the second utterance is greater than or equal to a set threshold value, the electronic device (101) may determine parameters for processing the voice signal information of the second utterance. For example, the electronic device (101) may determine parameters for processing the voice signal of the second utterance with different values ​​from the parameters for processing the voice signal of the first utterance.

[0110] For example, the voice signal processing unit (520) may process the second utterance according to the determined parameter. For example, the electronic device (101) may determine the energy level of the second utterance as a parameter based on the energy level of the first utterance and the set energy level. The voice signal processing unit (520) may increase or decrease the energy level of the voice signal of the second utterance according to the determined parameter.

[0111] For example, if the energy level of the first utterance is about 50, the energy level of the voice signal of the second utterance is about 60, and the performance of voice recognition increases as the energy level approaches about 100, the electronic device (101) can determine a parameter so that the energy level of the voice signal of the second utterance becomes about 100. The voice signal processing unit (520) can process the second utterance according to the parameter determined so that the energy level of the second utterance becomes about 100. The voice signal processing unit (520) can process the second utterance so that the energy level of the second utterance is amplified according to the determined parameter.

[0112] For example, the electronic device (101) can determine parameters for processing the second utterance based on the signal-to-noise ratio (SNR). For example, if the set SNR is 20 dB, the SNR of the first utterance is about 0 dB, and the SNR of the second utterance is about 3 dB, the electronic device (101) can determine parameters so that the SNR of the second utterance becomes about 20 dB.

[0113] For example, the electronic device (101) can perform noise removal of the second utterance using noise information of the first utterance.

[0114] In the above example, the electronic device (101) has described an operation of determining parameters for processing the second utterance based on the energy level and / or SNR of the second utterance. However, the electronic device (101) is not limited to the above example, and the electronic device (101) may determine parameters to determine various voice signal characteristics of the second utterance. For example, the electronic device (101) may determine parameters to determine voice signal characteristics such as voice rate and voice section of the second utterance. The voice signal processing unit (520) may process the second utterance according to the determined parameters, and may amplify / reduce the energy level of the second utterance, determine the SNR of the second utterance, or determine the voice rate and voice section of the second utterance.

[0115] For example, the electronic device (101) can determine the first similarity between the recognition result of the first utterance and the recognition result of the second utterance using the repeated utterance determination unit (560). The electronic device (101) can determine the first similarity in terms of the character similarity between the recognition result of the first utterance and the recognition result of the second utterance. Hereinafter, the recognition result of the first utterance may be a corrected recognition result output from the recognition result output unit (550) by the electronic device (101) processing the first utterance. For convenience of explanation, the corrected recognition result corresponding to the first utterance output from the recognition result output unit (550) will be described as the recognition result of the first utterance.

[0116] For example, the repeated utterance judgment unit (560) can judge the first similarity based on the main keywords and format of the recognition result of the first utterance and the recognition result of the second utterance.

[0117] For example, if the recognition result of the first utterance is "Call Cutie." and the recognition result of the second utterance is "Call Cute.", the repeated utterance determination unit (560) can determine the first similarity based on the degree of similarity between the main keywords 'Cutie' and 'Cute'.

[0118] For example, if the recognition result of the first utterance is "Call Cutie." and the recognition result of the second utterance is "Call Cute.", the repeated utterance determination unit (560) can determine the first similarity based on the degree to which the format of the recognition results "Call ~" is similar.

[0119] For example, if the recognition result of the first utterance is "Call Cutie." and the recognition result of the second utterance is "Call Cute.", the repeated utterance determination unit (560) can determine the first similarity based on the degree to which the recognition result of the first utterance and the recognition result of the second utterance are similar.

[0120] For example, if the first similarity is greater than or equal to a set threshold, the electronic device (101) may determine a parameter for correcting the recognition result of the second utterance. For example, the parameter for correcting the recognition result may include an edit distance for correcting the entity name included in the recognition result of the second utterance.

[0121] For example, if the user's first and second utterances are "Call Kutie." and the recognition result of the first utterance and the recognition result of the second utterance are "Call Cute.", the electronic device (101) can increase the edit distance by a set amount. For example, if the threshold value of the set edit distance is 1 and the edit distance between the entity names 'Cute' and 'Kutie' is 2, the recognition result post-processing unit (54) cannot correct 'Cute' to 'Kutie' because the edit distance between 'Cute' and 'Kutie' is greater than the threshold value of the set edit distance.

[0122] If the recognition result of the first utterance and the recognition result of the second utterance are "Call Cute.", the electronic device (101) can identify that the second utterance is a repetition of the first utterance by the user. If the first similarity is greater than or equal to a set threshold, the electronic device (101) can identify that the second utterance is a repetition of the first utterance. The electronic device (101) can increase the correction threshold of the edit distance.

[0123] In the above example, if the threshold value of the edit distance is increased to 2, the recognition result post-processing unit (540) can correct the recognition result of the second utterance to “Call Kutie.”

[0124] For example, the electronic device (101) can correct the recognition result of the second utterance based on the data stored in the memory (130) or the personal database (PD). For example, if the recognition result of the first utterance and the recognition result of the second utterance are "Call Cute." and the determined edit distance threshold (e.g., the set edit distance threshold or the edit distance threshold obtained by increasing the initial value of the edit distance (e.g., 1) by the set size (e.g., 1)) is 2, the recognition result post-processing unit (540) can correct the recognition result of the second utterance to an entity name stored in the PD whose edit distance is within 2 from 'Cute'. For example, if the entity name 'Kutie' is stored in the PD and the edit distance between 'Cute' and 'Kutie' is 2, the recognition result post-processing unit (540) can correct the recognition result of the second utterance to "Call Kutie.".

[0125] For example, the electronic device (101) can correct the recognition result of the second utterance differently from the recognition result of the first utterance based on the determined parameter. For example, if the recognition result of the first utterance and the recognition result of the second utterance are "Call Cute.", the determined edit distance is 2, and the entity names 'Cute' and 'Kutie' are stored in the PD, the electronic device (101) can correct the recognition result of the second utterance to "Call Kutie.". Although the entity name 'Cute' that is identical to the entity name 'Cute' included in the recognition result of the second utterance (e.g., has an edit distance of 0) is stored in the PD, since "Call Cute." is identical to the recognition result of the first utterance, the recognition result post-processing unit (540) can correct the recognition result of the second utterance to "Call Kutie.".

[0126] For example, the electronic device (101) can determine the second similarity between the recognition result of the corrected second utterance and the recognition result of the first utterance. If the second similarity is greater than or equal to a set threshold, the electronic device (101) can correct candidates for the recognition result of the second utterance based on the parameter. The electronic device (101) can determine a recognition result different from the recognition result of the first utterance among the corrected candidates as the recognition result of the second utterance.

[0127] For example, there may be multiple candidates for the recognition result of the second utterance. In the following description, it is assumed that the candidates for the recognition result of the second utterance include the first-ranked candidate "Call Cute.", the second-ranked candidate "Call Cutie.", the third-ranked candidate "Call Kutie.", and the fourth-ranked candidate "Call Kute."

[0128] For example, if the recognition result of the first utterance is "Call Cute." and the determined edit distance is 2, the electronic device (101) can correct the first-ranked candidate "Call Cute." based on the determined edit distance. For example, if the edit distance between the entity names 'Kutie' and 'Cute' stored in the PD is 3, the electronic device (101) can determine and / or correct the recognition result of the second utterance as "Call Cute."

[0129] The electronic device (101) can determine a second similarity between the recognition result of the corrected second utterance and the recognition result of the first utterance. Since the recognition result of the corrected second utterance and the recognition result of the first utterance are identical, the second similarity may be greater than or equal to a set threshold value.

[0130] If the second similarity is greater than or equal to the set threshold, the electronic device (101) can correct the recognition result candidates of the second utterance based on the determined parameter (e.g., the determined edit distance). For example, the recognition result post-processing unit (540) can correct the second-ranked candidate "Call Cutie." based on the entity name stored in the PD and the determined edit distance. For example, if the edit distance between 'Cutie' and 'Kutie' is 2 or less and the entity name 'Kutie' is the entity name stored in the PD, the recognition result post-processing unit (540) can correct the second-ranked candidate "Call Cutie." to "Call Kutie."

[0131] The recognition result post-processing unit (540) can correct the third-ranked candidate "Call Kutie." and the fourth-ranked candidate "Call Kute." in substantially the same way as correcting the first-ranked candidate and / or the second-ranked candidate based on the entity name stored in the PD and the determined parameters.

[0132] For example, the electronic device (101) may determine a recognition result different from the recognition result of the first utterance among the corrected candidates as the recognition result of the second utterance. For example, if the first-ranked candidate corrected by the recognition result post-processing unit (540) is "Call Cute.", the corrected second-ranked candidate is "Call Kutie.", the corrected third-ranked candidate is "Call Kutie.", and the corrected fourth-ranked candidate is "Call Kutie.", the electronic device (101) may determine the corrected second-ranked candidate "Call Kutie." as the recognition result of the second utterance. Since the corrected first-ranked candidate is the same as the recognition result "Call Cute." of the first utterance, the electronic device (101) may determine the corrected second-ranked candidate "Call Kutie.", which is different from the recognition result of the first utterance and has the highest priority, as the recognition result of the second utterance.

[0133] For example, the electronic device (101) may determine the candidate with the highest priority among the corrected candidates as the recognition result of the second utterance. For example, if the first-priority candidate corrected by the recognition result post-processing unit (540) is "Call Cute.", the corrected second-priority candidate is "Call Cute.", the corrected third-priority candidate is "Call Kutie.", and the corrected fourth-priority candidate is "Call Kutie.", the electronic device (101) may determine the corrected third-priority candidate "Call Kutie." as the recognition result of the second utterance. Since the corrected first-priority candidate and the corrected second-priority candidate are the same as the recognition result "Call Cute." of the first utterance, the electronic device (101) may determine the corrected third-priority candidate "Call Kutie.", which is different from the recognition result of the first utterance and has the highest priority, as the recognition result of the second utterance.

[0134] In the above example, the electronic device (101) has described an embodiment in which the second similarity is determined using the recognition result of the corrected second utterance and the recognition result of the first utterance, but is not limited thereto. For example, the electronic device (101) may receive a third utterance within a set time after receiving the second utterance. The electronic device (101) may determine the second similarity using the recognition result of the corrected third utterance and the recognition result of the first utterance. If the second similarity is greater than or equal to a set threshold using the recognition result of the corrected third utterance and the recognition result of the first utterance, the electronic device (101) may correct the recognition result candidates of the third utterance. The electronic device (101) may determine a recognition result different from the recognition result of the first utterance among the corrected candidates as the recognition result of the third utterance.

[0135] For example, if the first similarity between the recognition result of the first utterance and the recognition result of the second utterance is greater than or equal to a set threshold, the electronic device (101) may determine a parameter for correcting the recognition result of the second utterance. For example, the electronic device (101) may increase the set edit distance (e.g., 1) by a set amount (e.g., 1). The determined parameter may be initialized to the set edit distance if no subsequent utterance is received within a set time.

[0136] For example, if the first similarity between the recognition result of the second utterance and the recognition result of the first utterance is greater than or equal to a set threshold, the electronic device (101) may increase the set edit distance from 1 to 2. For example, if the third utterance is not received within a set time after receiving the second utterance (or after outputting the recognition result of the second utterance), the electronic device (101) may initialize the (determined) edit distance to 1 (or initialize the parameter). For example, if the first similarity between the recognition result of the third utterance received within a set time after receiving the second utterance and the recognition result of the first utterance (and / or the recognition result of the second utterance) is less than a set threshold, the electronic device (101) may initialize the (determined) edit distance to 1. For example, if the first similarity between the recognition result of the third utterance received within a set time after the reception of the second utterance and the recognition result of the first utterance (and / or the recognition result of the second utterance) is greater than or equal to a set threshold, the electronic device (101) may increase the (determined) edit distance from 2 to 3.

[0137] The recognition result output unit (550) can output the recognition result of the corrected second utterance (or third utterance), or the corrected candidate determined as the recognition result of the second utterance (or third utterance).

[0138]

[0139] Figure 6 is a flowchart of an operation of a voice signal processing method according to various embodiments.

[0140] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0141] According to one embodiment, operations (610) to (670) may be understood to be performed in a processor (120) (e.g., processor (120) of FIGS. 1 and 2) of an electronic device (101) (e.g., electronic device (101) of FIGS. 1, 2, and 5).

[0142] For example, the electronic device (101) may store the recognition result of the first utterance received from the user in operation (610). For example, the recognition result of the first utterance may represent a recognition result output from the recognition result output unit (e.g., the recognition result output unit (550) of FIG. 5) of the electronic device (101) corresponding to the received first utterance.

[0143] For example, the electronic device (101) may receive a second utterance from the user within a set time period after receiving the first utterance in operation (620). For example, in operation (620), the electronic device (101) may receive the second utterance within a set time period after outputting the recognition result of the first utterance.

[0144] For example, the electronic device (101) may determine a first similarity between the recognition result of the first utterance and the recognition result of the second utterance in operation (630). For example, the electronic device (101) may determine the first similarity from the perspective of character similarity. The electronic device (101) may determine the first similarity based on at least one of the entirety, main keyword, and format of the recognition result of the first utterance and the recognition result of the second utterance.

[0145] The electronic device (101) can compare at least one of the entirety, main keywords, and format of the recognition result of the first utterance and the second recognition result using various known methods, and determine the degree to which the recognition result of the first utterance and the recognition result of the second utterance are similar.

[0146] For example, the first similarity may indicate the degree to which the recognition result of the first utterance is similar to the recognition result of the second utterance.

[0147] For example, the electronic device (101) can determine whether the first similarity is greater than or equal to a threshold value in operation (640).

[0148] For example, if it is determined that the first similarity is greater than or equal to the threshold value in operation (640), the electronic device (101) may determine a parameter for correcting the recognition result of the second utterance in operation (650). For example, the parameter for correcting the recognition result of the second utterance may include an edit distance. The edit distance may refer to a parameter for correcting the entity name included in the recognition result to another entity name (e.g., an entity name stored in the PD). As the edit distance increases, the electronic device (101) may correct the entity name included in the recognition result to an entity name that is significantly different from the entity name included in the recognition result.

[0149] For example, if the entity name included in the recognition result is 'Cute', if the edit distance is 1, the electronic device (101) cannot correct the entity name to 'Kutie', but if the edit distance is 2, the electronic device (101) can correct the entity name to 'Kutie'.

[0150] In the above example, the edit distance between entity names is exemplary, and the electronic device (101) can determine an edit distance between entity names (or words) other than the above example, and the edit distance between entity names in the above example may be determined differently.

[0151] For example, the electronic device (101) may increase the set edit distance (e.g., 1) in operation (650) by a set size (e.g., 1). If no subsequent utterance is input within a set time after the recognition result of the second utterance is output (or after the second utterance is received), the increased (or determined) edit distance may be initialized.

[0152] For example, the electronic device (101) may correct the recognition result of the second utterance based on the parameters determined in operation (660). For example, the electronic device (101) may correct the recognition result of the second utterance based on data or PD stored in a memory (e.g., memory (130) of FIG. 1). For example, the electronic device (101) may correct the recognition result of the second utterance differently from the recognition result of the first utterance.

[0153] For example, if the first similarity is determined to be less than the threshold value in operation (640), the electronic device (101) can correct the recognition result of the second utterance based on the parameter (or parameter initial value) set in operation (670).

[0154]

[0155] FIG. 7 is a flowchart illustrating an operation for determining a recognition result of a second utterance by using candidates for the recognition result of the second utterance according to various embodiments.

[0156] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0157] According to one embodiment, operations (710) to (740) may be understood to be performed in a processor (120) (e.g., processor (120) of FIGS. 1 and 2) of an electronic device (101) (e.g., electronic device (101) of FIGS. 1, 2, and 5).

[0158] For example, after operation (660), the electronic device (101) can determine the second similarity between the recognition result of the second utterance corrected in operation (710) and the recognition result of the first utterance. For example, the electronic device (101) can determine the second similarity between the recognition result of the second utterance corrected in substantially the same manner as operation (630) of FIG. 6 (operation of determining the first similarity between the recognition result of the first utterance and the recognition result of the second utterance).

[0159] For example, the electronic device (101) can determine whether the second similarity is greater than or equal to a set threshold value in operation (720).

[0160] For example, if the second similarity is greater than or equal to a set threshold in operation (720), the electronic device (101) may correct the candidates for the recognition result of the second utterance based on the parameters in operation (730). For example, the electronic device (101) may generate a set number of candidates for the recognition result of the second utterance. The candidates for the recognition result of the second utterance may be prioritized based on the probability that they match the second utterance received from the user.

[0161] If the second similarity is greater than or equal to the set threshold, the electronic device (101) can correct candidates (e.g., second-ranked candidate, third-ranked candidate, fourth-ranked candidate) of the recognition result of the second utterance based on the determined parameter in operation (730).

[0162] For example, the electronic device (101) may determine a recognition result different from the recognition result of the first utterance among the corrected candidates in operation (740) as the recognition result of the second utterance. For example, if the corrected second-rank candidate and the corrected third-rank candidate are identical to the recognition result of the first utterance (or the recognition result of the corrected second utterance), the electronic device (101) may determine the corrected fourth-rank candidate as the recognition result of the second utterance.

[0163] For example, the electronic device (101) can compare the corrected candidates with the recognition result of the first utterance (or the recognition result of the corrected second utterance) to determine the third similarity. The electronic device (101) can determine the corrected candidate with the highest priority among the corrected candidates whose third similarity is lower than a set threshold as the recognition result of the second utterance.

[0164] In the above examples, the thresholds for comparison with the first similarity, the second similarity, and the third similarity may be set to different values, or may be set to the same value.

[0165] For example, if the second similarity in operation (720) is less than the set threshold, the electronic device (101) can output the recognition result of the corrected second utterance.

[0166]

[0167]

[0168] Figure 8 is a flowchart of an operation of a voice signal processing method according to various embodiments.

[0169] In the following examples, the operations may be performed sequentially, but are not necessarily sequential. For example, the order of the operations may be changed, and at least two operations may be performed in parallel.

[0170] According to one embodiment, operations (810) to (870) may be understood to be performed in a processor (120) (e.g., processor (120) of FIGS. 1 and 2) of an electronic device (101) (e.g., electronic device (101) of FIGS. 1, 2, and 5).

[0171] For example, the electronic device (101) can store the first utterance received from the user in operation (810).

[0172] For example, the electronic device (101) can receive a second utterance from the user within a set time after receiving the first utterance in operation (820).

[0173] With respect to the above operations (810) and (820), the descriptions of operations (610) and (620) of FIG. 6 can be applied substantially identically.

[0174] For example, the electronic device (101) can determine the similarity between the first utterance and the second utterance in operation (830). The electronic device (101) can determine the similarity in terms of signal similarity of the voice signal in operation (830).

[0175] For example, the electronic device (101) can determine similarity based on the voice signal information of the first utterance and the voice signal information of the second utterance. The voice signal information can include information such as energy, speed, and the end point of the voice section.

[0176] In operation (830), the electronic device (101) may determine the similarity between the first utterance and the second utterance using various voice signal information, without being limited to the aforementioned examples. In operation (830), the electronic device (101) may determine the similarity between the voice signal of the first utterance and the voice signal of the second utterance using various known methods.

[0177] For example, the electronic device (101) can determine whether the similarity is greater than a set threshold value in operation (840).

[0178] For example, if it is determined that the similarity is greater than a set threshold value in operation (840), the electronic device (101) may determine parameters for processing the second utterance in operation (850). The electronic device (101) may process the first utterance and / or the second utterance based on the parameters, thereby determining the voice characteristics of the first utterance and / or the voice characteristics of the second utterance.

[0179] For example, the electronic device (101) may determine parameters for processing the second utterance in operation (850) differently from parameters for processing the first utterance (e.g., initially set parameters).

[0180] For example, the electronic device (101) can determine the voice characteristics of the second utterance based on the parameters determined in operation (860).

[0181] For example, if the voice signal information of the first utterance and the voice signal information of the second utterance are the same, the parameters for processing the second utterance may be determined to have different values ​​from the parameters for processing the first utterance. If the parameters for processing the second utterance are different from the parameters for processing the first utterance, even if the voice signal information of the first utterance and the voice signal information of the second utterance are the same, the voice characteristics of the second utterance may be different from the voice characteristics of the first utterance.

[0182] For example, if the parameters for processing the second utterance are different from the parameters for processing the first utterance, even if the voice signal information of the first utterance and the voice signal information of the second utterance are the same, the energy level and SNR of the second utterance may be determined differently from the energy level and SNR of the first utterance.

[0183] For example, if it is determined that the similarity is less than the set threshold in operation (840), the electronic device (101) can determine the voice characteristics of the second utterance based on the parameters set in operation (870).

[0184]

[0185] FIG. 9 is a diagram showing a screen in which an electronic device (101) (e.g., the electronic device (101) of FIG. 1 and FIG. 2) according to various embodiments provides a recognition result (920) of a first utterance and a recognition result (930) of a corrected second utterance.

[0186] As shown in FIG. 9, the electronic device (101) can display a screen (910) through a display included in a display module (e.g., display module (160) of FIG. 1).

[0187] For example, the recognition result (920) of the first utterance and the recognition result of the second utterance may be "Call Cute." The electronic device (101) may determine a parameter for correcting the second utterance if the first similarity between the recognition result (920) of the first utterance and the recognition result of the second utterance is greater than or equal to a set threshold. The electronic device (101) may correct the recognition result of the second utterance to "Call Kutie." based on the determined parameter.

[0188] The electronic device (101) can display the recognition result (920) of the first utterance and the recognition result (930) of the corrected second utterance on the display and provide them to the user, as shown in FIG. 9.

[0189] For example, the electronic device (101) may perform an operation based on at least one of the recognition result (920) of the first utterance and the recognition result (930) of the corrected second utterance, based on an input (e.g., touch) received from the user. For example, if the user selects the recognition result (920) of the first utterance, the electronic device (101) may connect a call to the contact of 'Cute' stored in the PD. For example, if the user selects the recognition result (930) of the corrected second utterance, the electronic device (101) may connect a call to the contact of 'Kutie' stored in the PD.

[0190]

[0191] FIG. 10 is a diagram illustrating an operation of an electronic device (e.g., the electronic device (101) of FIG. 1 and FIG. 5) according to various embodiments to correct candidates for recognition results of a second utterance based on parameters.

[0192] For example, the first utterance (1010) may be “Call Lee Hyun-ji,” and the recognition result of the first utterance (1010) may be “Call Lee Young-ji.” The entity names stored in the PD (or PDSS (personal data sync service)) may be “Lee Hyun-ji” or “Lee Yeon-ji.”

[0193] For example, the first-ranked candidate for the recognition result of the first utterance (1010) may be “Call Lee Young-ji,” the second-ranked candidate may be “Call Lee Yeon-ji,” the third-ranked candidate may be “Call Lee Hyun-ji,” and the fourth-ranked candidate may be “Call Lee Yeon-jin.” If the set edit distance is 1, the edit distance between the entity name ‘Lee Hyun-ji’ stored in the PD and the entity name ‘Lee Young-ji’ is 2, and the edit distance between the entity name ‘Lee Yeon-ji’ stored in the PD and the entity name ‘Lee Young-ji’ is 1, the electronic device (101) may determine the (corrected) recognition result (1020) of the first utterance as “Call Lee Yeon-ji” (which has the highest priority and the smallest edit distance between the entity name of the recognition result and the entity name stored in the PD).

[0194] If the second utterance (1030) is “Call Lee Hyun-ji,” the first-ranked candidate for the recognition result of the second utterance may be “Call Lee Young-ji,” the second-ranked candidate may be “Call Lee Yeon-ji,” the third-ranked candidate may be “Call Lee Hyun-ji,” and the fourth-ranked candidate may be “Call Lee Yeon-jin.”

[0195] Based on the similarity between the (corrected) recognition result (1020) of the first utterance (“Call Yeonji Lee”) and the recognition result of the second utterance (or the first-ranked recognition result “Call Yeonji Lee”), the electronic device (101) can increase the edit distance to 2.

[0196] For example, the electronic device (101) can correct the first-ranked candidate "Call Lee Yeon-ji" of the recognition result of the second utterance to "Call Lee Yeon-ji" based on the set edit distance of 1. In Fig. 10, the first-ranked recognition result of the second utterance can be "Call Lee Yeon-ji."

[0197] For example, the electronic device (101) can compare the corrected recognition result (1020) of the first utterance (“Call Yeonji Lee”) with the first-ranked recognition result of the second utterance (“Call Yeonji Lee”) to determine the similarity.

[0198] Since the corrected recognition result (1020) of the first utterance (“Call Yeonji Lee”) and the corrected first-rank recognition result (“Call Yeonji Lee”) of the second utterance are identical, the similarity may be greater than a set threshold. If the corrected recognition result (1020) of the first utterance (“Call Yeonji Lee”) and the corrected first-rank recognition result (“Call Yeonji Lee”) of the second utterance are identical, the electronic device (101) may increase the set edit distance 1 by a set size 1.

[0199] The edit distance between the entity "Lee Young-ji" and the entity "Lee Hyeon-ji" stored in the PDSS may be 2, and the edit distance between the entity "Lee Yeon-ji" stored in the PDSS may be 1. When the determined edit distance is 2, the electronic device (101) may correct (or determine) the entity ("Lee Young-ji") included in the first-ranking candidate of the second utterance to the entity ("Lee Yeon-ji") stored in the PDSS (e.g., the entity with the smallest edit distance among the entities stored in the PDSS). The electronic device (101) may correct (or determine) the first-ranking recognition result of the second utterance to "Call Lee Yeon-ji."

[0200] The edit distance between the entity "Lee Yeon-ji" and the entity "Lee Hyeon-ji" stored in the PDSS may be 1, and the edit distance between the entity "Lee Yeon-ji" stored in the PDSS may be 0. When the determined edit distance is 2, the electronic device (101) may correct (or determine) the entity ("Lee Yeon-ji") included in the second-ranking candidate of the second utterance to the entity ("Lee Yeon-ji") stored in the PDSS (e.g., the entity with the smallest edit distance among the entities stored in the PDSS). The electronic device (101) may correct (or determine) the second-ranking recognition result of the second utterance to "Call Lee Yeon-ji."

[0201] The edit distance between the entity "Lee Hyun-ji" and the entity "Lee Hyun-ji" stored in the PDSS may be 0, and the edit distance between the entity "Lee Yeon-ji" stored in the PDSS may be 1. When the determined edit distance is 2, the electronic device (101) may correct (or determine) the entity ("Lee Hyun-ji") included in the third-ranking candidate of the second utterance to the entity ("Lee Hyun-ji") stored in the PDSS (e.g., the entity with the smallest edit distance among the entities stored in the PDSS). The electronic device (101) may correct (or determine) the third-ranking recognition result of the second utterance to "Call Lee Hyun-ji."

[0202] The edit distance between the entity "Lee Yeon-jin" and the entity "Lee Hyeon-ji" stored in the PDSS may be 1, and the edit distance between the entity "Lee Yeon-ji" stored in the PDSS may be 0. When the determined edit distance is 2, the electronic device (101) may correct (or determine) the entity ("Lee Yeon-jin") included in the 4th rank candidate of the second utterance to the entity ("Lee Yeon-ji") stored in the PDSS (e.g., the entity with the smallest edit distance among the entities stored in the PDSS). The electronic device (101) may correct (or determine) the 4th rank recognition result of the second utterance to "Call Lee Yeon-ji."

[0203] In the example above, when the edit distance is 2, the electronic device (101) can determine the first-rank recognition result, the second-rank recognition result, and the fourth-rank recognition result of the second utterance as “Call Lee Yeon-ji,” and determine the third-rank recognition result of the second utterance as “Call Lee Hyeon-ji.”

[0204] For example, the electronic device (101) may determine a recognition result different from the corrected recognition result (1020) of the first utterance among candidates for the recognition result of the second utterance as the corrected recognition result (1060) of the second utterance.

[0205] For example, in the above example, the first-rank recognition result, the second-rank recognition result, and the fourth-rank recognition result of the second utterance (“Call Lee Yeon-ji”) are the same as the corrected recognition result (1020) of the first utterance, and the third-rank recognition result (“Call Lee Hyeon-ji”) of the second utterance is different from the recognition result (1020) of the first utterance, so the electronic device (101) can determine the corrected recognition result (1040) of the second utterance as “Call Lee Hyeon-ji,” which is the third-rank recognition result of the second utterance.

[0206] For example, the electronic device (101) may determine the candidate for the recognition result of the second utterance with the highest priority among the candidates for the recognition result of the second utterance, the corrected recognition result (1020) of the first utterance, and the other recognition results, as the corrected recognition result (1060) of the second utterance.

[0207] For example, if the corrected recognition result of the first utterance is “Call Lee Yeon-ji,” the first-priority recognition result and the fourth-priority recognition result of the second utterance are “Call Lee Yeon-ji,” the second-priority recognition result is “Call Lee Hyeon-ji,” and the third-priority recognition result of the second utterance is “Call Lee Young-ji,” the electronic device (101) may determine the second-priority recognition result of the second utterance, which is different from the corrected recognition result of the first utterance and has a higher priority, as the corrected recognition result of the second utterance.

[0208]

[0209] FIG. 11 is a diagram illustrating an operation of an electronic device (e.g., the electronic device (101) of FIG. 1 and FIG. 5) according to various embodiments to correct candidates for recognition results of a second utterance based on parameters.

[0210] In Figure 11, the first utterance (1110) may be "Call to Cutie", and the second utterance (1130) may be "Call to Cutie". For example, entity names stored in the PDSS may include "Cutie", "Cote", and "Kutie".

[0211] The electronic device (101) can correct the recognition result of the first utterance, “Call to Kute”, based on the parameters for correction and the entity name stored in the PDSS.

[0212] For example, the edit distance between the entity name "Kute" and the entity names "Cutie", "Cote", and "Kutie" stored in the PD may be 2.

[0213] Since there is no entity stored in the PDSS that has an edit distance of less than 1 from the entity “Kute” included in the recognition result of the first utterance, the electronic device (101) may correct (or determine) the recognition result of the first utterance to “Call to Kute.” In Fig. 11, the corrected recognition result (1120) of the first utterance may be “Call to Kute.”

[0214] If the recognition result of the second utterance is “Call to Kute,” the first similarity between the corrected recognition result (1120) of the first utterance and the recognition result of the second utterance can be determined. Since the corrected recognition result (1120) of the first utterance (“Call to Kute”) and the recognition result of the second utterance (“Call to Kute”) are identical, the electronic device (101) can increase the edit distance to 2 based on the second similarity.

[0215] If there are multiple entity names (e.g., "Cutie", "Cote", "Kutie") stored in PDSSs that have an edit distance of 2 or less from the entity name "Kute" included in the recognition result of the second utterance, the electronic device (101) can provide the user with the recognition result of the second utterance with the multiple entity names corrected or the multiple entity names as shown in FIG. 11. Based on the user's selection, the electronic device (101) can determine the recognition result of the second utterance. For example, the electronic device (101) can display the recognition result of the second utterance with the multiple entity names corrected (e.g., "Call to Cutie", "Call to Cote", "Call to Kutie") or the multiple entity names (e.g., "Cutie", "Cote", "Kutie") on the display module.

[0216] The electronic device (101) may receive from the user a recognition result of a second utterance that has been corrected to include multiple entity names, or an input for selecting one of the multiple entity names. For example, when receiving an input for selecting "Call to Kute" or "Kute" from the user, the electronic device (101) may correct (or determine) the corrected recognition result (1140) of the second utterance to "Call to Kute."

[0217]

[0218] An electronic device (101) according to various embodiments may include a processor (120) and a memory (130) electrically coupled to the processor (120) and storing instructions executable by the processor (120). The processor (120) may store a recognition result of a first utterance received from a user when the instruction is executed. The processor (120) may receive a second utterance from the user within a set time after receiving the first utterance. The processor (120) may determine a first similarity between the recognition result of the first utterance and the recognition result of the second utterance. When the first similarity is equal to or greater than a set threshold, the processor (120) may determine a parameter for correcting the recognition result of the second utterance. The processor (120) may correct the recognition result of the second utterance based on the parameter.

[0219] The processor (120) may determine a second similarity between the recognition result of the corrected second utterance and the recognition result of the first utterance. If the second similarity is greater than or equal to a set threshold, the processor (120) may correct candidates for the recognition result of the second utterance based on the parameter. The processor (120) may determine, among the corrected candidates, a recognition result different from the recognition result of the first utterance as the recognition result of the second utterance.

[0220] The above processor (120) can determine the candidate with the highest priority among the corrected candidates as the recognition result of the second utterance.

[0221] The processor (120) can correct the recognition result of the second utterance based on the data stored in the memory (130).

[0222] The processor (120) can provide the user with the recognition result of the first utterance and the recognition result of the corrected second utterance. The processor (120) can perform an operation according to the input received from the user.

[0223] The above processor (120) can increase the edit distance for correction of the entity name included in the recognition result of the second utterance.

[0224] The above processor (120) can determine the first similarity based on the main keywords and format of the recognition result of the first utterance and the recognition result of the second utterance.

[0225] An electronic device (101) according to various embodiments may include a processor (120) and a memory (130) electrically coupled to the processor (120) and storing instructions executable by the processor (120). The processor (120) may store a first utterance received from a user when the instruction is executed. The processor (120) may receive a second utterance from the user within a set time after receiving the first utterance. The processor (120) may determine a similarity between the first utterance and the second utterance. If the similarity is greater than or equal to a set threshold, the processor (120) may determine a parameter for processing the second utterance. The processor (120) may determine a voice characteristic of the second utterance based on the parameter.

[0226] The processor (120) can determine the similarity based on information of the voice signal of the first utterance and information of the voice signal of the second utterance.

[0227] The processor (120) can determine the voice characteristics of the second speech by processing at least one of the energy level, signal to noise ratio (SNR), and speed of the second speech according to the parameter.

[0228] A voice signal processing method according to various embodiments may include an operation of storing a recognition result of a first utterance received from a user, an operation of receiving a second utterance from the user within a set time after receiving the first utterance, an operation of determining a first similarity between a recognition result of the first utterance and a recognition result of the second utterance, an operation of determining a parameter for correcting a recognition result of the second utterance when the first similarity is equal to or greater than a set threshold value, and an operation of correcting a recognition result of the second utterance based on the parameter.

[0229] The speech signal processing method may further include an operation of determining a second similarity between the recognition result of the corrected second utterance and the recognition result of the first utterance. The speech signal processing method may further include an operation of correcting candidates of the recognition result of the second utterance based on the parameter when the second similarity is equal to or greater than a set threshold value. The speech signal processing method may further include an operation of determining, among the corrected candidates, a recognition result different from the recognition result of the first utterance as the recognition result of the second utterance.

[0230] The operation of determining the recognition result of the second utterance may include an operation of determining the candidate with the highest priority among the corrected candidates as the recognition result of the second utterance.

[0231] The operation of correcting the recognition result of the second utterance may include an operation of correcting the recognition result of the second utterance based on data stored in the memory (130).

[0232] The above-described voice signal processing method may further include an operation of providing the user with the recognition result of the first utterance and the recognition result of the corrected second utterance. The method may further include an operation of performing an action according to an input received from the user.

[0233] The operation of determining a parameter for correcting the recognition result of the second utterance may include an operation of increasing an edit distance for correction of an entity name included in the recognition result of the second utterance.

[0234] The operation of determining the first similarity may include an operation of determining the first similarity based on the main keywords and format of the recognition result of the first utterance and the recognition result of the second utterance.

[0235] A voice signal processing method according to various embodiments may include an operation of storing a first utterance received from a user, an operation of receiving a second utterance from the user within a set time after receiving the first utterance, an operation of determining a similarity between the first utterance and the second utterance, an operation of determining a parameter for processing the second utterance when the similarity is greater than or equal to a set threshold value, and an operation of determining a voice characteristic of the second utterance based on the parameter.

[0236] A computer-readable recording medium according to various embodiments may record a program for executing the above-described voice signal processing method.

[0237]

[0238] Electronic devices according to the various embodiments disclosed in this document may take various forms. Electronic devices may include, for example, portable communication devices (e.g., smartphones), computer devices, portable multimedia devices, portable medical devices, cameras, wearable devices, or home appliances. Electronic devices according to the embodiments of this document are not limited to the aforementioned devices.

[0239] The various embodiments of this document and the terminology used therein are not intended to limit the technical features described in this document to specific embodiments, but should be understood to include various modifications, equivalents, or substitutes of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise. In this document, each of the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can include any one of the items listed together in the corresponding phrase among those phrases, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish one component from another, and do not limit the components in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as "coupled" or "connected" to another (e.g., a second component), with or without the terms "functionally" or "communicatively," it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0240] The term "module" used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0241] Various embodiments of the present document may be implemented as software (e.g., a program (140)) including one or more instructions stored in a storage medium (e.g., an internal memory (136) or an external memory (138)) readable by a machine (e.g., an electronic device (101)). For example, a processor (e.g., a processor (120)) of the machine (e.g., an electronic device (101)) may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the machine to operate to perform at least one function according to the at least one called instruction. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, 'non-transitory' simply means that the storage medium is a tangible device and does not contain signals (e.g., electromagnetic waves), and the term does not distinguish between cases where data is stored semi-permanently or temporarily on the storage medium.

[0242] According to one embodiment, the method according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smart phones). In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0243] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and arranged in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

Claims

1. In an electronic device (101), Processor (120); and A memory (130) electrically coupled to the processor (120) and storing instructions executable by the processor (120) Including, The above processor (120) When the above command is executed, the recognition result of the first utterance received from the user is stored; Receive a second utterance from the user within a set time after receiving the first utterance; Determine the first similarity between the recognition result of the first utterance and the recognition result of the second utterance; If the first similarity is greater than or equal to the set threshold, a parameter for correcting the recognition result of the second utterance is determined, Based on the above parameters, the recognition result of the second utterance is corrected. Electronic device (101).

2. In paragraph 1, The above processor (120) Determine the second similarity between the recognition result of the above-mentioned corrected second utterance and the recognition result of the above-mentioned first utterance; If the second similarity is greater than or equal to a set threshold, candidates for the recognition result of the second utterance are corrected based on the parameter; Among the above-mentioned corrected candidates, a recognition result different from the recognition result of the first utterance is determined as the recognition result of the second utterance. Electronic device (101).

3. In paragraph 2, The above processor (120) Among the above-mentioned corrected candidates, the candidate with the highest priority is determined as the recognition result of the second utterance. Electronic device (101).

4. In paragraph 1, The above processor (120) Based on the data stored in the above memory (130), the recognition result of the second utterance is corrected. Electronic device (101).

5. In paragraph 1, The above processor (120) Providing the user with the recognition result of the first utterance and the recognition result of the corrected second utterance; Performing an action based on input received from the user; Electronic device (101).

6. In paragraph 1, The above processor (120) Increasing the edit distance for correction of the entity name included in the recognition result of the second utterance above, Electronic device (101).

7. In paragraph 1, The above processor (120) Based on the main keywords and formats of the recognition result of the first utterance and the recognition result of the second utterance, the first similarity is determined. Electronic devices (101).

8. An action of saving the recognition result of the first utterance received from the user; An action of receiving a second utterance from the user within a set time after receiving the first utterance; An operation of determining a first similarity between the recognition result of the first utterance and the recognition result of the second utterance; An operation for determining a parameter for correcting the recognition result of the second utterance when the first similarity is greater than or equal to the set threshold value; and An operation for correcting the recognition result of the second utterance based on the above parameters. Including, A method for processing voice signals.

9. In paragraph 8, An operation of determining a second similarity between the recognition result of the above-mentioned corrected second utterance and the recognition result of the above-mentioned first utterance; If the second similarity is greater than or equal to a set threshold value, an operation of correcting candidates for the recognition result of the second utterance based on the parameter; and An action of determining a recognition result different from the recognition result of the first utterance among the above-mentioned corrected candidates as the recognition result of the second utterance; Including more, A method for processing voice signals.

10. In paragraph 9, The action determined by the recognition result of the above second utterance is: An action of determining the candidate with the highest priority among the above-mentioned corrected candidates as the recognition result of the second utterance. Including, A method for processing voice signals.

11. In any one of paragraphs 8 to 10, The operation of correcting the recognition result of the above second utterance is as follows: An operation for correcting the recognition result of the second utterance based on the data stored in the memory (130). Including, A method for processing voice signals.

12. In any one of paragraphs 8 to 11, An operation of providing the user with the recognition result of the first utterance and the recognition result of the corrected second utterance; and An action that performs an action based on input received from the user. Including more, A method for processing voice signals.

13. In any one of paragraphs 8 to 12, The operation of determining the parameters for correcting the recognition result of the above second utterance is as follows: An operation for increasing the edit distance for correction of the entity name included in the recognition result of the second utterance Including, A method for processing voice signals.

14. In any one of paragraphs 8 to 13, The above first similarity determination operation is: An operation for judging the first similarity based on the main keywords and formats of the recognition result of the first utterance and the recognition result of the second utterance Including, A method for processing voice signals.

15. A computer-readable recording medium having recorded thereon a program for executing a voice signal processing method according to any one of claims 8 to 14.

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