WO2024071946A1 - Procédé de traduction basé sur une caractéristique vocale et dispositif électronique associé - Google Patents

Procédé de traduction basé sur une caractéristique vocale et dispositif électronique associé Download PDF

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Publication number
WO2024071946A1
WO2024071946A1 PCT/KR2023/014738 KR2023014738W WO2024071946A1 WO 2024071946 A1 WO2024071946 A1 WO 2024071946A1 KR 2023014738 W KR2023014738 W KR 2023014738W WO 2024071946 A1 WO2024071946 A1 WO 2024071946A1
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Prior art keywords
electronic device
voice
text
processor
speech
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PCT/KR2023/014738
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English (en)
Korean (ko)
Inventor
신호선
송가진
Original Assignee
삼성전자 주식회사
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Priority claimed from KR1020220127120A external-priority patent/KR20240043021A/ko
Application filed by 삼성전자 주식회사 filed Critical 삼성전자 주식회사
Publication of WO2024071946A1 publication Critical patent/WO2024071946A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G06F3/048—Interaction techniques based on graphical user interfaces [GUI]
    • G06F3/0481—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance
    • G06F3/04817—Interaction techniques based on graphical user interfaces [GUI] based on specific properties of the displayed interaction object or a metaphor-based environment, e.g. interaction with desktop elements like windows or icons, or assisted by a cursor's changing behaviour or appearance using icons
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/40—Processing or translation of natural language
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/02—Feature extraction for speech recognition; Selection of recognition unit
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/22—Procedures used during a speech recognition process, e.g. man-machine dialogue
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L15/00—Speech recognition
    • G10L15/26—Speech to text systems
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/04—Training, enrolment or model building
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L17/00—Speaker identification or verification techniques
    • G10L17/06—Decision making techniques; Pattern matching strategies
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/48—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use
    • G10L25/51—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00 specially adapted for particular use for comparison or discrimination
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/78—Detection of presence or absence of voice signals
    • G10L25/87—Detection of discrete points within a voice signal
    • G—PHYSICS
    • G10—MUSICAL INSTRUMENTS; ACOUSTICS
    • G10L—SPEECH ANALYSIS TECHNIQUES OR SPEECH SYNTHESIS; SPEECH RECOGNITION; SPEECH OR VOICE PROCESSING TECHNIQUES; SPEECH OR AUDIO CODING OR DECODING
    • G10L25/00—Speech or voice analysis techniques not restricted to a single one of groups G10L15/00 - G10L21/00
    • G10L25/93—Discriminating between voiced and unvoiced parts of speech signals

Definitions

  • Embodiments disclosed in this document relate to a translation method based on speech features and an electronic device therefor.
  • the electronic device may be configured to provide translation or interpretation.
  • the electronic device may acquire the user's voice (eg, speech) and provide translation or interpretation corresponding to the user's speech.
  • An electronic device can acquire a voice signal from a user, perform voice recognition on the voice signal, and obtain text corresponding to the voice signal.
  • the electronic device can provide translation corresponding to the user's speech by providing translated text for the text.
  • the electronic device may provide interpretation through audio output based on the translated text.
  • translation based on machine learning can be used.
  • translation may be performed using a model learned using a transformer.
  • the electronic device may obtain a text translated from the first language into the second language by inputting the text of the first language into a learned model.
  • the electronic device can perform translation in units of one sentence or multiple words.
  • Users can translate speech in real time using a translation application installed on the electronic device. For example, a user can translate the conversation partner's speech into his or her native language, or translate his or her own speech into the other person's native language.
  • the user can cause the electronic device to listen to the speech by performing input to the electronic device.
  • the electronic device can activate the microphone based on user input and acquire speech through the activated microphone. Even if there is no user input to end listening, the electronic device can end listening. For example, when the end point of the speech is identified, the electronic device can deactivate the microphone and proceed with translation of the acquired speech.
  • Users can communicate with others who speak different languages. For example, a user can translate his or her speech using a translation application for conversation.
  • the electronic device can identify the endpoint from the user's speech and end listening.
  • the other party can give a speech after listening is completed. In this case, the user may need to activate the listening of the translation application again and request the other party to speak again.
  • An electronic device may include a microphone, a display, memory, and a processor.
  • Memory can store instructions executable by the processor.
  • the processor may activate the microphone based on a first user input to activate speech translation.
  • the processor may receive an audio signal through an activated microphone and detect a first end point from the first voice signal included in the audio signal.
  • the processor may display the first text based on a speech recognition result for the first speech signal preceding the first endpoint and the translated first text corresponding to the first text on the display.
  • the processor may maintain the microphone activated for at least a first period of time following the first endpoint.
  • the processor When at least a portion of the second voice signal is detected from an audio signal acquired using a microphone within a first time interval, the processor detects a second endpoint of the second voice signal, and detects a second voice signal preceding the second endpoint.
  • the second voice characteristic of the signal may be compared with the first voice characteristic of the first voice signal. If the first voice characteristic and the second voice characteristic do not match, the processor may display the second text based on the voice recognition result for the second voice signal. If the first voice characteristic and the second voice characteristic match, the processor may translate the second text and the second text into the target language and display the translated second text.
  • the operation of activating the microphone of the electronic device based on a first user input to activate voice translation, and transmitting an audio signal through the activated microphone An operation of receiving, an operation of detecting a first end point from a first voice signal included in an audio signal, a first text and a first text based on a voice recognition result for the first voice signal preceding the first end point It may include displaying the translated first text corresponding to on the display of the electronic device.
  • the method further comprises, when at least a portion of the second speech signal is detected using the microphone within a first time interval subsequent to the first endpoint, combining the second speech characteristic of the second speech signal and the first speech characteristic of the first speech signal. May include comparison operations.
  • the method may include displaying a second text based on a speech recognition result for the second speech signal if the first speech characteristic and the second speech characteristic do not match based on the comparison.
  • the method may include, when the first voice characteristic and the second voice characteristic match, translating the second text and the second text into the target language and displaying the translated second text.
  • An electronic device can provide smoother translation through extended listening time.
  • An electronic device can provide translation that meets the user's intent by determining whether to translate based on voice characteristics.
  • An electronic device can increase user convenience through a translation application.
  • FIG. 1 is a block diagram of an electronic device in a network environment according to various embodiments.
  • Figure 2 is a block diagram showing an integrated intelligence system according to an embodiment.
  • Figure 3 is a diagram showing how relationship information between concepts and actions is stored in a database, according to an embodiment.
  • 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.
  • Figure 5 illustrates a translation environment using an electronic device according to an embodiment.
  • Figure 6 shows a block diagram of an electronic device according to an embodiment.
  • FIG. 7A shows configurations of an electronic device according to an embodiment.
  • FIG. 7B shows configurations of an electronic device and a peripheral electronic device according to an embodiment.
  • Figure 8 shows an example of a conversation for which translation is provided.
  • Figure 9 shows an example of a conversation for which translation is provided.
  • Figure 10 shows an example of a conversation for which translation is provided.
  • FIG. 11 illustrates a translation UI of an electronic device according to an embodiment.
  • FIG. 12 illustrates a translation UI of an electronic device according to an embodiment.
  • FIG. 13 illustrates a translation UI of an electronic device according to an embodiment.
  • Figure 14 illustrates a translation UI of an electronic device according to an embodiment.
  • Figure 15 shows a flowchart of a method for providing translation on an electronic device according to an embodiment.
  • Figure 16 shows a flowchart of a method for providing translation on an electronic device according to an embodiment.
  • FIG. 1 is a block diagram of an electronic device 101 in a network environment 100, according to various embodiments.
  • the electronic device 101 communicates with the electronic device 102 through a first network 198 (e.g., a short-range wireless communication network) or a second network 199. It is possible to communicate with at least one of the electronic device 104 or the server 108 through (e.g., a long-distance wireless communication network). According to one embodiment, the electronic device 101 may communicate with the electronic device 104 through the server 108.
  • a first network 198 e.g., a short-range wireless communication network
  • a second network 199 e.g., a second network 199.
  • the electronic device 101 may communicate with the electronic device 104 through the server 108.
  • the electronic device 101 includes a processor 120, a memory 130, an input module 150, an audio output module 155, a display module 160, an audio module 170, and a sensor module ( 176), interface 177, connection terminal 178, haptic module 179, camera module 180, power management module 188, battery 189, communication module 190, subscriber identification module 196 , or may include an antenna module 197.
  • at least one of these components eg, the connection terminal 178) may be omitted or one or more other components may be added to the electronic device 101.
  • some of these components e.g., sensor module 176, camera module 180, or antenna module 197) are integrated into one component (e.g., display module 160). It can be.
  • the processor 120 for example, executes software (e.g., program 140) to operate at least one other component (e.g., hardware or software component) of the electronic device 101 connected to the processor 120. It can be controlled and various data processing or calculations can be performed. According to one embodiment, as at least part of data processing or computation, the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132. The commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134.
  • software e.g., program 140
  • the processor 120 stores commands or data received from another component (e.g., sensor module 176 or communication module 190) in volatile memory 132.
  • the commands or data stored in the volatile memory 132 can be processed, and the resulting data can be stored in the non-volatile memory 134.
  • the processor 120 includes a main processor 121 (e.g., a central processing unit or an application processor) or an auxiliary processor 123 that can operate independently or together (e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor).
  • a main processor 121 e.g., a central processing unit or an application processor
  • auxiliary processor 123 e.g., a graphics processing unit, a neural network processing unit ( It may include a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.
  • the electronic device 101 includes a main processor 121 and a secondary processor 123
  • the secondary processor 123 may be set to use lower power than the main processor 121 or be specialized for a designated function. You can.
  • the auxiliary processor 123 may be implemented separately from the main processor 121 or as part of it.
  • the auxiliary processor 123 may, for example, act on behalf of the main processor 121 while the main processor 121 is in an inactive (e.g., sleep) state, or while the main processor 121 is in an active (e.g., application execution) state. ), together with the main processor 121, at least one of the components of the electronic device 101 (e.g., the display module 160, the sensor module 176, or the communication module 190) At least some of the functions or states related to can be controlled.
  • co-processor 123 e.g., image signal processor or communication processor
  • may be implemented as part of another functionally related component e.g., camera module 180 or communication module 190. there is.
  • the auxiliary processor 123 may include a hardware structure specialized for processing artificial intelligence models.
  • Artificial intelligence models can be created through machine learning. For example, such learning may be performed in the electronic device 101 itself on which the artificial intelligence model is performed, or may be performed through a separate server (e.g., server 108).
  • Learning algorithms may include, for example, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but It is not limited.
  • An artificial intelligence model may include multiple artificial neural network layers.
  • Artificial neural networks include deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), restricted boltzmann machine (RBM), belief deep network (DBN), bidirectional recurrent deep neural network (BRDNN), It may be one of deep Q-networks or a combination of two or more of the above, but is not limited to the examples described above.
  • artificial intelligence models may additionally or alternatively include software structures.
  • the memory 130 may store various data used by at least one component (eg, the processor 120 or the sensor module 176) of the electronic device 101. Data may include, for example, input data or output data for software (e.g., program 140) and instructions related thereto.
  • Memory 130 may include volatile memory 132 or non-volatile memory 134.
  • the program 140 may be stored as software in the memory 130 and may include, for example, an operating system 142, middleware 144, or application 146.
  • the input module 150 may receive commands or data to be used in a component of the electronic device 101 (e.g., the processor 120) from outside the electronic device 101 (e.g., a user).
  • the input module 150 may include, for example, a microphone, mouse, keyboard, keys (eg, buttons), or digital pen (eg, stylus pen).
  • the sound output module 155 may output sound signals to the outside of the electronic device 101.
  • the sound output module 155 may include, for example, a speaker or a receiver. Speakers can be used for general purposes such as multimedia playback or recording playback.
  • the receiver can be used to receive incoming calls. According to one embodiment, the receiver may be implemented separately from the speaker or as part of it.
  • the display module 160 can visually provide information to the outside of the electronic device 101 (eg, a user).
  • the display module 160 may include, for example, a display, a hologram device, or a projector, and a control circuit for controlling the device.
  • the display module 160 may include a touch sensor configured to detect a touch, or a pressure sensor configured to measure the intensity of force generated by the touch.
  • the audio module 170 can convert sound into an electrical signal or, conversely, convert an electrical signal into sound. According to one embodiment, the audio module 170 acquires sound through the input module 150, the sound output module 155, or an external electronic device (e.g., directly or wirelessly connected to the electronic device 101). Sound may be output through the electronic device 102 (e.g., speaker or headphone).
  • the electronic device 102 e.g., speaker or headphone
  • the sensor module 176 detects the operating state (e.g., power or temperature) of the electronic device 101 or the external environmental state (e.g., user state) and generates an electrical signal or data value corresponding to the detected state. can do.
  • the sensor module 176 includes, for example, a gesture sensor, a gyro sensor, an air pressure sensor, a magnetic sensor, an acceleration sensor, a grip sensor, a proximity sensor, a color sensor, an IR (infrared) sensor, a biometric sensor, It may include a temperature sensor, humidity sensor, or light sensor.
  • the interface 177 may support one or more designated protocols that can be used to connect the electronic device 101 directly or wirelessly with an external electronic device (eg, the electronic device 102).
  • 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.
  • HDMI high definition multimedia interface
  • USB universal serial bus
  • SD card interface Secure Digital Card interface
  • audio interface audio interface
  • connection terminal 178 may include a connector through which the electronic device 101 can be physically connected to an external electronic device (eg, the electronic device 102).
  • the connection terminal 178 may include, for example, an HDMI connector, a USB connector, an SD card connector, or an audio connector (eg, a headphone connector).
  • the haptic module 179 can convert electrical signals into mechanical stimulation (e.g., vibration or movement) or electrical stimulation that the user can perceive through tactile or kinesthetic senses.
  • the haptic module 179 may include, for example, a motor, a piezoelectric element, or an electrical stimulation device.
  • the camera module 180 can capture still images and moving images.
  • the camera module 180 may include one or more lenses, image sensors, image signal processors, or flashes.
  • the power management module 188 can manage power supplied to the electronic device 101.
  • the power management module 188 may be implemented as at least a part of, for example, a power management integrated circuit (PMIC).
  • PMIC power management integrated circuit
  • the battery 189 may supply power to at least one component of the electronic device 101.
  • the battery 189 may include, for example, a non-rechargeable primary battery, a rechargeable secondary battery, or a fuel cell.
  • Communication module 190 is configured to provide a direct (e.g., wired) communication channel or wireless communication channel between electronic device 101 and an external electronic device (e.g., electronic device 102, electronic device 104, or server 108). It can support establishment and communication through established communication channels. Communication module 190 operates independently of processor 120 (e.g., an application processor) and may include one or more communication processors that support direct (e.g., wired) communication or wireless communication.
  • processor 120 e.g., an application processor
  • the communication module 190 is 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., : LAN (local area network) communication module, or power line communication module) may be included.
  • 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
  • GNSS global navigation satellite system
  • wired communication module 194 e.g., : LAN (local area network) communication module, or power line communication module
  • the corresponding communication module is 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., legacy It may communicate with an external electronic device 104 through a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN).
  • a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN).
  • a telecommunication network such as a cellular network, a 5G network, a next-generation communication network, the Internet, or a computer network (e.g., LAN or WAN).
  • a telecommunication network such as a cellular network, a 5G network, a next-generation communication network
  • the wireless communication module 192 uses subscriber information (e.g., International Mobile Subscriber Identifier (IMSI)) stored in the subscriber identification module 196 within a communication network such as the first network 198 or the second network 199.
  • subscriber information e.g., International Mobile Subscriber Identifier (IMSI)
  • IMSI International Mobile Subscriber Identifier
  • the wireless communication module 192 may support 5G networks after 4G networks and next-generation communication technologies, for example, NR access technology (new radio access technology).
  • NR access technology provides high-speed transmission of high-capacity data (eMBB (enhanced mobile broadband)), minimization of terminal power and access to multiple terminals (mMTC (massive machine type communications)), or high reliability and low latency (URLLC (ultra-reliable and low latency). -latency communications)) can be supported.
  • the wireless communication module 192 may support high frequency bands (eg, mmWave bands), for example, to achieve high data rates.
  • the wireless communication module 192 uses various technologies to secure performance in high frequency bands, for example, beamforming, massive array multiple-input and multiple-output (MIMO), and full-dimensional multiplexing. It can support technologies such as input/output (FD-MIMO: full dimensional MIMO), array antenna, analog beam-forming, or large scale antenna.
  • the wireless communication module 192 may support various requirements specified in the electronic device 101, an external electronic device (e.g., electronic device 104), or a network system (e.g., second network 199).
  • the wireless communication module 192 supports Peak data rate (e.g., 20 Gbps or more) for realizing eMBB, loss coverage (e.g., 164 dB or less) for realizing mmTC, or U-plane latency (e.g., 164 dB or less) for realizing URLLC.
  • Peak data rate e.g., 20 Gbps or more
  • loss coverage e.g., 164 dB or less
  • U-plane latency e.g., 164 dB or less
  • the antenna module 197 may transmit signals or power to or receive signals or power from the outside (e.g., an external electronic device).
  • the antenna module 197 may include an antenna including a radiator made of a conductor or a conductive pattern formed on a substrate (eg, PCB).
  • the antenna module 197 may include a plurality of antennas (eg, 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 is connected to the plurality of antennas by, for example, the communication module 190. can be selected. Signals or power may be transmitted or received between the communication module 190 and an external electronic device through the at least one selected antenna.
  • other components eg, radio frequency integrated circuit (RFIC) may be additionally formed as part of the antenna module 197.
  • RFIC radio frequency integrated circuit
  • a mmWave antenna module includes: a printed circuit board, an RFIC disposed on or adjacent to a first side (e.g., bottom side) of the printed circuit board and capable of supporting a designated high frequency band (e.g., mmWave band); And a plurality of antennas (e.g., array antennas) disposed on or adjacent to the second side (e.g., top or side) of the printed circuit board and capable of transmitting or receiving signals in the designated high frequency band. can do.
  • a first side e.g., bottom side
  • a designated high frequency band e.g., mmWave band
  • a plurality of antennas e.g., array antennas
  • peripheral devices e.g., bus, general purpose input and output (GPIO), serial peripheral interface (SPI), or mobile industry processor interface (MIPI)
  • signal e.g. commands or data
  • commands or data may be transmitted or received between the electronic device 101 and the external electronic device 104 through the server 108 connected to the second network 199.
  • Each of the external electronic devices 102 or 104 may be of the same or different type as the electronic device 101.
  • all or part of the operations performed in the electronic device 101 may be executed in one or more of the external electronic devices 102, 104, or 108.
  • the electronic device 101 may perform the function or service instead of executing the function or service on its own.
  • one or more external electronic devices may be requested to perform at least part of the function or service.
  • One or more external electronic devices that have received the request may execute at least part 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 part of a response to the request.
  • cloud computing distributed computing, mobile edge computing (MEC), or client-server computing technology can be used.
  • the electronic device 101 may provide an ultra-low latency service using, for example, distributed computing or mobile edge computing.
  • the external electronic device 104 may include an Internet of Things (IoT) device.
  • Server 108 may be an intelligent server using machine learning and/or neural networks.
  • the external electronic device 104 or server 108 may be included in the second network 199.
  • the electronic device 101 may be applied to intelligent services (e.g., smart home, smart city, smart car, or healthcare) based on 5G communication technology and IoT-related technology.
  • Electronic devices may be of various types.
  • 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 embodiments of this document are not limited to the above-described devices.
  • first, second, or first or second may be used simply to distinguish one component from another, and to refer to that component in other respects (e.g., importance or order) is not limited.
  • One (e.g., first) component is said to be “coupled” or “connected” to another (e.g., second) component, with or without the terms “functionally” or “communicatively.”
  • any of the components can be connected to the other components directly (e.g. wired), wirelessly, or through a third component.
  • module used in various embodiments of this document may include a unit implemented in hardware, software, or firmware, and is interchangeable with terms such as logic, logic block, component, or circuit, for example. It can be used as A module may be an integrated part or a minimum unit of the parts or a part thereof that performs one or more functions. For example, according to one embodiment, the module may be implemented in the form of an application-specific integrated circuit (ASIC).
  • ASIC application-specific integrated circuit
  • Various embodiments of the present document are one or more instructions stored in a storage medium (e.g., built-in memory 136 or external memory 138) that can be read by a machine (e.g., electronic device 101). It may be implemented as software (e.g., program 140) including these.
  • a processor e.g., processor 120
  • the one or more instructions may include code generated by a compiler or code that can be executed by an interpreter.
  • a storage medium that can be read by a device may be provided in the form of a non-transitory storage medium.
  • 'non-transitory' only means that the storage medium is a tangible device and does not contain signals (e.g. electromagnetic waves), and this term refers to cases where data is semi-permanently stored in the storage medium. There is no distinction between temporary storage cases.
  • Computer program products are commodities and can be traded between sellers and buyers.
  • 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 through an application store (e.g. Play StoreTM) or on two user devices (e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online.
  • a machine-readable storage medium e.g. compact disc read only memory (CD-ROM)
  • an application store e.g. Play StoreTM
  • two user devices e.g. It can be distributed (e.g. downloaded or uploaded) directly between smart phones) or online.
  • at least a portion of the computer program product may be at least temporarily stored or temporarily created in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.
  • each component (e.g., module or program) of the above-described components may include a single or plural entity, and some of the plurality of entities may be separately placed in other components. there is.
  • one or more of the components or operations described above may be omitted, or one or more other components or operations may be added.
  • multiple components eg, modules or programs
  • the integrated component may perform one or more functions of each component of the plurality of components identically or similarly to those performed by the corresponding component of the plurality of components prior to the integration. .
  • 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, or omitted. Alternatively, one or more other operations may be added.
  • Figure 2 is a block diagram showing an integrated intelligence system according to an embodiment.
  • the integrated intelligence system of one embodiment may include a user terminal 201, an intelligent server 300, and a service server 400.
  • the user terminal 201 (e.g., the electronic device 101 in FIG. 1) of one embodiment may be a terminal device (or electronic device) capable of connecting to the Internet, for example, a mobile phone, a smartphone, or a personal digital assistant (PDA). It may be a digital assistant, a laptop computer, a television (TV), a white appliance, a wearable device, a head mounted device (HMD), or a smart speaker.
  • a terminal device or electronic device capable of connecting to the Internet
  • PDA personal digital assistant
  • TV television
  • white appliance a white appliance
  • HMD head mounted device
  • the user terminal 201 may include a communication interface 290, a microphone 270, a speaker 255, a display 260, a memory 230, and/or a processor 220. there is.
  • the components listed above may be operatively or electrically connected to each other.
  • the communication interface 290 may be connected to an external device and configured to transmit and receive data.
  • the microphone 270 e.g., the audio module 170 of FIG. 1 may receive sound (e.g., a user's speech) and convert it into an electrical signal.
  • the speaker 255 e.g., the sound output module 155 of FIG. 1 may output an electrical signal as sound (e.g., voice).
  • Display 260 e.g., display module 160 of FIG. 1) may be configured to display images or video.
  • the display 260 of one embodiment may also display a graphic user interface (GUI) of an app (or application program) being executed.
  • GUI graphic user interface
  • the memory 230 may store a client module 231, a software development kit (SDK) 233, and a plurality of applications.
  • the client module 231 and SDK 233 may form a framework (or solution program) for performing general functions. Additionally, the client module 231 or SDK 233 may configure a framework for processing voice input.
  • the plurality of applications may be programs for performing designated functions.
  • the plurality of applications may include a first app 235a and/or a second app 235b.
  • each of the plurality of applications may include a plurality of operations to perform a designated function.
  • the applications may include an alarm app, a messaging app, and/or a schedule app.
  • a plurality of applications are executed by the processor 220 to sequentially execute at least some of the plurality of operations.
  • the processor 220 in one embodiment may control the overall operation of the user terminal 201.
  • the processor 220 may be electrically connected to the communication interface 290, microphone 270, speaker 255, and display 260 to perform designated operations.
  • processor 220 may include at least one processor.
  • the processor 220 of one embodiment may also execute a program stored in the memory 230 to perform a designated function.
  • the processor 220 may execute at least one of the client module 231 or the SDK 233 and perform the following operations for processing voice input.
  • the processor 220 may control the operation of a plurality of applications through, for example, the SDK 233.
  • the following operations described as operations of the client module 231 or SDK 233 may be operations performed by execution of the processor 220.
  • the client module 231 in one embodiment may receive voice input.
  • the client module 231 may receive a voice signal corresponding to a user utterance detected through the microphone 270.
  • the client module 231 may transmit the received voice input (eg, voice signal) to the intelligent server 300.
  • the client module 231 may transmit status information of the user terminal 201 to the intelligent server 300 along with the received voice input.
  • the status information may be, for example, execution status information of an app.
  • the client module 231 of one embodiment may receive a result corresponding to the received voice input from the intelligent server 300. For example, if the intelligent server 300 can calculate a result corresponding to the received voice input, the client module 231 may receive a result corresponding to the received voice input. The client module 231 may display the received result on the display 260.
  • the client module 231 of one embodiment may receive a plan corresponding to the received voice input.
  • the client module 231 can display the results of executing multiple operations of the app according to the plan on the display 260.
  • the client module 231 may sequentially display execution results of a plurality of operations on a display.
  • the user terminal 201 may display only some results of executing a plurality of operations (eg, the result of the last operation) on the display.
  • the client module 231 may receive a request from the intelligent server 300 to obtain information necessary to calculate a result corresponding to the voice input. According to one embodiment, the client module 231 may transmit the necessary information to the intelligent server 300 in response to the request.
  • the client module 231 in one embodiment may transmit information as a result of executing a plurality of operations according to the plan to the intelligent server 300.
  • the intelligent server 300 can use the result information to confirm that the received voice input has been processed correctly.
  • the client module 231 in one embodiment may include a voice recognition module. According to one embodiment, the client module 231 may recognize voice input performing a limited function through the voice recognition module. For example, the client module 231 may execute an intelligent app for processing voice input by performing an organic action in response to a designated voice input (e.g., wake up!).
  • a voice recognition module e.g., a voice recognition module
  • the client module 231 may recognize voice input performing a limited function through the voice recognition module.
  • the client module 231 may execute an intelligent app for processing voice input by performing an organic action in response to a designated voice input (e.g., wake up!).
  • the intelligent server 300 receives information related to the user voice input from the user terminal 201 through the network 299 (e.g., the first network 198 and/or the second network 199 in FIG. 1). You can receive it. According to one embodiment, the intelligent server 300 may change data related to the received voice input into text data. According to one embodiment, the intelligent server 300 may generate at least one plan for performing a task corresponding to the user's voice input based on the text data.
  • the plan may be generated by an artificial intelligence (AI) system.
  • An artificial intelligence system may be a rule-based system, a neural network-based system (e.g., a feedforward neural network (FNN)), and/or a recurrent neural network. network(RNN))). Alternatively, it may be a combination of the above or a different artificial intelligence system.
  • a 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, an artificial intelligence system can select at least one plan from a plurality of predefined plans.
  • the intelligent server 300 of one embodiment may transmit a result according to the generated plan to the user terminal 201 or transmit the generated plan to the user terminal 201.
  • the user terminal 201 may display results according to the plan on the display 260.
  • the user terminal 201 may display the results of executing an operation according to the plan on the display 260.
  • the intelligent server 300 of one embodiment includes a front end 310, a natural language platform 320, a capsule database 330, an execution engine 340, It may include an end user interface (350), a management platform (360), a big data platform (370), or an analytic platform (380).
  • the front end 310 of one embodiment may receive a voice input received by the user terminal 201 from the user terminal 201 .
  • the front end 310 may transmit a response corresponding to the voice input to the user terminal 201.
  • the natural language platform 320 includes an automatic speech recognition module (ASR module) 321, a natural language understanding module (NLU module) 323, and a planner module ( It may include a planner module (325), a natural language generator module (NLG module) (327), and/or a text to speech module (TTS module) (329).
  • ASR module automatic speech recognition module
  • NLU module natural language understanding module
  • planner module It may include a planner module (325), a natural language generator module (NLG module) (327), and/or a text to speech module (TTS module) (329).
  • the automatic voice recognition module 321 of one embodiment may convert voice input received from the user terminal 201 into text data.
  • the natural language understanding module 323 in one embodiment may determine the user's intention using text data of voice input. For example, the natural language understanding module 323 may perform syntactic analysis and/or semantic analysis to determine the user's intention.
  • the natural language understanding module 323 in one embodiment determines the meaning of a word extracted from a voice input using linguistic features (e.g., grammatical elements) of a morpheme or phrase, and matches the meaning of the identified word to the user's intention. You can determine your intention.
  • the planner module 325 in one embodiment may generate a plan using the intent and parameters determined by the natural language understanding module 323. According to one embodiment, the planner module 325 may determine a plurality of domains required to perform the task based on the determined intention. The planner module 325 may determine a plurality of operations included in each of the plurality of domains determined based on the intention. According to one embodiment, the planner module 325 may determine parameters required to execute the determined plurality of operations or result values output by executing the plurality of operations. The parameters and the result values may be defined as concepts of a specified type (or class). Accordingly, the plan may include a plurality of operations and/or a plurality of concepts determined by the user's intention.
  • the planner module 325 may determine the relationship between the plurality of operations and the plurality of concepts in a stepwise (or hierarchical) manner. For example, the planner module 325 may determine the execution order of a plurality of operations determined based on the user's intention based on a plurality of concepts. In other words, the planner module 325 may determine the execution order of the plurality of operations based on the parameters required for execution of the plurality of operations and the results output by executing the plurality of operations. Accordingly, the planner module 325 may generate a plan that includes association information (eg, ontology) between a plurality of operations and a plurality of concepts. The planner module 325 can create a plan using information stored in the capsule database 330, which stores a set of relationships between concepts and operations.
  • association information eg, ontology
  • the natural language generation module 327 of one embodiment may change specified information into text form.
  • the information changed to the text form may be in the form of natural language speech.
  • the text-to-speech conversion module 329 in one embodiment can change information in text form into information in voice form.
  • the user terminal 201 may include an automatic speech recognition module and/or a natural language understanding module. After the user terminal 201 recognizes the user's voice command, it can transmit text information corresponding to the recognized voice command to the intelligent server 300.
  • the user terminal 201 may include a text-to-speech module. The user terminal 201 may receive text information from the intelligent server 300 and output the received text information as voice.
  • the capsule database 330 may store information about the relationship between a plurality of concepts and operations corresponding to a plurality of domains.
  • the capsule may include a plurality of action objects (or action information) and/or concept objects (or concept information) included in the plan.
  • the capsule database 330 may store a plurality of capsules in the form of CAN (concept action network).
  • a plurality of capsules may be stored in a function registry included in the capsule database 330.
  • the capsule database 330 may include a strategy registry in which strategy information necessary for determining a plan corresponding to a voice input is stored.
  • the strategy information may include standard information for determining one plan when there are multiple plans corresponding to voice input.
  • the capsule database 330 may include a follow up registry in which information on follow-up actions is stored to suggest follow-up actions to the user in a specified situation.
  • the follow-up action may include, for example, follow-up speech.
  • the capsule database 330 may include a layout registry that stores layout information of information output through the user terminal 201.
  • the capsule database 330 may include a vocabulary registry where vocabulary information included in capsule information is stored.
  • the capsule database 330 may include a dialogue registry in which information about dialogue (or interaction) with a user is stored.
  • the capsule database 330 can update stored objects through a developer tool.
  • the developer tool may include, for example, a function editor for updating operation objects or concept objects.
  • the developer tool may include a vocabulary editor for updating the vocabulary.
  • the developer tool may include a strategy editor that creates and registers a strategy for determining the plan.
  • the developer tool may include a dialogue editor that creates a dialogue with the user.
  • the developer tool may include a follow up editor that can edit follow-up utterances to activate follow-up goals and provide hints. The subsequent goal may be determined based on currently set goals, user preferences, or environmental conditions.
  • the capsule database 330 may also be implemented within the user terminal 201.
  • the execution engine 340 of one embodiment may calculate a result using the generated plan.
  • the end user interface 350 may transmit the calculated result to the user terminal 201. Accordingly, the user terminal 201 can receive the result and provide the received result to the user.
  • the management platform 360 of one embodiment can manage information used in the intelligent server 300.
  • the big data platform 370 in one embodiment may collect user data.
  • the analysis platform 380 of one embodiment may manage quality of service (QoS) of the intelligent server 300. For example, the analytics platform 380 can manage the components and processing speed (or efficiency) of the intelligent server 300.
  • QoS quality of service
  • the service server 400 in one embodiment may provide a designated service (eg, food ordering or hotel reservation) to the user terminal 201.
  • the service server 400 may be a server operated by a third party.
  • the service server 400 in one embodiment may provide the intelligent server 300 with information for creating a plan corresponding to the received voice input. The provided information may be stored in the capsule database 330. Additionally, the service server 400 may provide result information according to the plan to the intelligent server 300.
  • the service server 400 may communicate with the intelligent server 300 and/or the user terminal 201 through the network 299.
  • the service server 400 can communicate with the intelligent server 300 through a separate connection.
  • the service server 400 is shown as one server in FIG. 2, the embodiments of this document are not limited thereto. At least one of the services 401, 402, and 403 of the service server 400 may be implemented as a separate server.
  • the user terminal 201 can provide various intelligent services to the user in response to user input.
  • the user input may include, for example, input through a physical button, touch input, or voice input.
  • the user terminal 201 may provide a voice recognition service through an internally stored intelligent app (or voice recognition app).
  • the user terminal 201 recognizes a user utterance or voice input received through the microphone 270 and provides a service corresponding to the recognized voice input to the user. can do.
  • the user terminal 201 may perform a designated operation alone or together with the intelligent server 300 and/or the service server 400 based on the received voice input. For example, the user terminal 201 may run an app corresponding to a received voice input and perform a designated operation through the executed app.
  • the user terminal 201 when the user terminal 201 provides a service together with the intelligent server 300 and/or the service server 400, the user terminal 201 uses the microphone 270 to It is possible to detect an utterance and generate a signal (or voice data) corresponding to the detected user utterance.
  • the user terminal 201 may transmit the voice data to the intelligent server 300 using the communication interface 290.
  • the intelligent server 300 In response to a voice input received from the user terminal 201, the intelligent server 300 according to one embodiment provides a plan for performing a task corresponding to the voice input, or an operation according to the plan. can produce results.
  • the plan may include a plurality of operations for performing a task corresponding to a user's voice input and/or 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 multiple operations and/or multiple concepts.
  • the user terminal 201 in one embodiment may receive the response using the communication interface 290.
  • the user terminal 201 uses the speaker 255 to output a voice signal generated inside the user terminal 201 to the outside, or uses the display 260 to output an image generated inside the user terminal 201 to the outside. It can be output as .
  • Figure 3 is a diagram showing how relationship information between concepts and actions is stored in a database, according to an embodiment.
  • the capsule database (e.g., capsule database 330) of the intelligent server 300 may store capsules in CAN (concept action network) format.
  • the capsule database may store operations for processing tasks corresponding to the user's voice input, and parameters necessary for the operations in CAN (concept action network) format.
  • the capsule database may store a plurality of capsules (capsule A 331, capsule B 334) corresponding to each of a plurality of domains (eg, applications).
  • one capsule e.g., capsule A 331) may correspond to one domain (e.g., location (geo), application).
  • one capsule contains at least one capsule of a service provider (e.g., CP 1 (332), CP 2 (333), CP3 (335), and/or CP4 (336) to perform functions for the domain associated with the capsule. )) can correspond.
  • a service provider e.g., CP 1 (332), CP 2 (333), CP3 (335), and/or CP4 (336) to perform functions for the domain associated with the capsule.
  • one capsule may include at least one operation 330a and at least one concept 330b for performing a designated function.
  • the natural language platform 320 may create a plan for performing a task corresponding to the received voice input using the capsule stored in the capsule database 330.
  • the planner module 325 of the natural language platform can create a plan using capsules stored in the capsule database.
  • create a plan 337 using the operations 331a, 332a and concepts 331b, 332b of capsule A 331 and the operations 334a and concept 334b of capsule B 334. can do.
  • Figure 4 is a diagram illustrating a screen in which a user terminal processes voice input received through an intelligent app according to an embodiment.
  • the user terminal 201 can run an intelligent app to process user input through the intelligent server 300.
  • the user terminal 201 when the user terminal 201 recognizes a designated voice input (eg, wake up! or receives an input through a hardware key (eg, a dedicated hardware key), the user terminal 201 It can run intelligent apps to process input. For example, the user terminal 201 may run an intelligent app while executing a schedule app. According to one embodiment, the user terminal 201 may display an object (eg, an icon) 211 corresponding to an intelligent app on the display 260. According to one embodiment, the user terminal 201 may receive voice input through a user's utterance.
  • a designated voice input eg, wake up
  • a hardware key eg, a dedicated hardware key
  • the user terminal 201 may receive a voice input saying “Tell me this week’s schedule!”
  • the user terminal 201 may display a user interface (UI) 213 (e.g., input window) of an intelligent app displaying text data of a received voice input on the display.
  • UI user interface
  • the user terminal 201 may display a result corresponding to the received voice input on the display.
  • the user terminal 201 may receive a plan corresponding to the received user input and display ‘this week’s schedule’ on the display according to the plan.
  • Figure 5 illustrates a translation environment using an electronic device according to an embodiment.
  • the voice translation environment 500 may include an electronic device 501, a server device 511, and/or a peripheral electronic device 521.
  • the electronic device 501 may correspond to the electronic device 101 of FIG. 1 or the user terminal 201 of FIG. 2.
  • server device 511 may correspond to server 108 in FIG. 1 or intelligent server 300 in FIG. 2.
  • the peripheral electronic device 521 may correspond to the electronic device 102 of FIG. 1 .
  • the electronic device 501 may be referred to as a listener device that receives the utterance 590 of the speaker 599.
  • the electronic device 501 may include a translation application.
  • the translation application may be included in a voice assistant application (eg, the client module 231 in FIG. 2).
  • the electronic device 501 may receive the utterance 590 of the speaker 599 using a voice reception circuit (eg, the audio module 170 of FIG. 1).
  • the electronic device 501 may receive the speaker 599's utterance 590 using a peripheral electronic device 521 (e.g., augmented reality glasses, ear buds, or any listening device). You can.
  • a peripheral electronic device 521 e.g., augmented reality glasses, ear buds, or any listening device. You can.
  • a user may run the translation application on the electronic device 501 and then translate the utterance 590 using the translation application.
  • the electronic device 501 may acquire a voice signal corresponding to the utterance 590.
  • the electronic device 501 can perform automatic voice recognition on voice signals.
  • the electronic device 501 may directly perform automatic voice recognition.
  • the electronic device 501 may transmit a voice signal to the server device 511 and receive a voice recognition result for the voice signal from the server device 511. Based on the voice recognition result, the electronic device 501 may provide translation corresponding to the voice.
  • the electronic device 501 may translate the voice recognition result into the target language and provide the translation result to the user.
  • the electronic device 501 may be configured to provide translation results visually and/or tactilely.
  • the server device 511 may perform at least some operations for translation.
  • the server device 511 may receive a voice signal from the electronic device 501 and perform automatic voice recognition on the voice signal (e.g., using the automatic voice module 321 of FIG. 2). .
  • the server device 511 may be set to provide a translation result to the electronic device 501 based on a voice signal received from the electronic device 501.
  • electronic device 501 may perform translation without assistance from server device 511. In this case, the server device 511 may be omitted.
  • the translation environment 500 described above with reference to FIG. 5 is illustrative, and embodiments of the present disclosure are not limited thereto. Those skilled in the art will understand that the server device 511 and/or the peripheral electronic device 521 may be omitted. As will be described later, utterances of users other than the user (eg, speaker 599) may also be translated by the translation application of the electronic device 501.
  • Figure 6 shows a block diagram of an electronic device according to an embodiment.
  • the electronic device 501 may include a processor 620, memory 630, microphone 650, display 660, and/or communication circuit 690.
  • the processor 620 may correspond to the processor 120 of FIG. 1 or the processor 220 of FIG. 2.
  • memory 630 may correspond to memory 130 of FIG. 1 or memory 230 of FIG. 2 .
  • the microphone 650 may correspond to the audio module 170 of FIG. 1 or the microphone 270 of FIG. 2.
  • display 660 may correspond to display module 160 of FIG. 1 or display 260 of FIG. 2 .
  • the communication circuit 690 may correspond to the communication module 190 of FIG. 1 or the communication interface 290 of FIG. 2.
  • the configuration of the electronic device 601 in FIG. 6 is an example, and the electronic device 601 may further include a configuration not shown in FIG. 6 .
  • Processor 620 may be electrically, operatively, or functionally connected to memory 630, microphone 650, display 660, and/or communication circuitry 690. there is.
  • one component when one component is “operably” connected to another component, it may mean that one component is connected to operate the other component. For example, one component may actuate another component by transmitting a control signal to the other component, either directly or via another component.
  • one component when one component is “functionally” connected to another component, it may mean that one component is connected to execute the function of the other component. For example, one component may execute the function of another component by transmitting a control signal to the other component directly or via another component.
  • Memory 630 may store instructions. When executed by the processor 620, the instructions may cause the electronic device 501 to perform various operations. In this disclosure, the operation of the electronic device 501 may be referred to as an operation performed by the processor 620 by executing instructions stored in the memory 630.
  • the microphone 650 can convert sound signals into electrical signals.
  • the electronic device 501 may convert the electrical signal received from the microphone 650 into a digital signal and perform data processing based on the digital signal.
  • the communication circuit 690 may provide communication between the electronic device 501 and other electronic devices (eg, the server device 511 and/or the peripheral electronic device 521 of FIG. 5 ).
  • Communication circuitry 690 may support wired and/or wireless communication.
  • Communication circuitry 690 may support short-range wireless communication and/or long-range wireless communication.
  • Electronic device 501 may provide translation.
  • the electronic device 501 may acquire speech data using the microphone 650 or the communication circuit 690.
  • the electronic device 501 may obtain text data by performing automatic voice recognition on speech data.
  • the electronic device 501 may obtain translated text data by performing translation on text data.
  • the electronic device 501 may provide translated text data through, for example, the display 660.
  • the electronic device 501 may transmit text data to an external electronic device (eg, the server device 511 of FIG. 5) and receive translated text data from the external electronic device.
  • the electronic device 501 may provide the translated text through the display 660 or through a speaker (not shown).
  • the electronic device 501 may detect an endpoint from the user's utterance.
  • the electronic device 501 may maintain the activated state of the microphone 650 for a predetermined period of time when the endpoint is detected. Within a predetermined time after detecting the endpoint, the electronic device 501 can acquire a new utterance. In this case, the electronic device 501 may determine whether to translate the new utterance by comparing the speech characteristics of the utterance acquired before the end point and the new utterance acquired after the end point.
  • various examples of the translation method of the electronic device 501 may be described with reference to FIGS. 7A to 16 .
  • the electronic device 501 may include a display 660, a memory 630, and a processor 620.
  • the memory 630 may store instructions that, when executed by the processor 620, allow the processor 620 to perform various operations.
  • FIG. 7A shows configurations of an electronic device according to an embodiment.
  • the electronic device 501-1 in FIG. 7A is an example of the electronic device 501 in FIG. 6 .
  • the electronic device 501-1 of FIG. 7A may be an electronic device that includes the configuration of the electronic device 501 described above with reference to FIG. 6 .
  • the description of the electronic device 501-1 described later in relation to FIG. 7A may be equally applied to the electronic device 501 of FIG. 6.
  • the electronic device 501-1 may include a plurality of modules.
  • the components of the electronic device 501-1 described with reference to FIG. 7A may be software modules implemented by executing instructions stored in the memory 630 by the processor 620.
  • At least some of the modules of the electronic device 501-1 may be hardware modules.
  • the memory 630 may store the speaker identification model DB 745.
  • EPD end point detection
  • speaker identification module 740 speaker identification module 740
  • ASR module 750 translation module 760
  • execution module 770 execution module 770
  • microphone control module 780 It may be a software module.
  • the electronic device 501-1 may activate the microphone 650 based on user input.
  • the electronic device 501 may display the UI (eg, UI of FIG. 11 ) of the translation application on the display 660 according to the execution of the translation application.
  • the electronic device 501-1 may activate the microphone 650 when an input to the UI (eg, an input to the button 1110 of FIG. 11) is received.
  • the electronic device 501-1 may activate the microphone 650 using the microphone control module 780.
  • the electronic device 501-1 can acquire an audio signal.
  • an audio signal may be referred to as digitized audio information or an analog audio signal, unless otherwise specified.
  • a voice signal may refer to a signal corresponding to a voice band among audio signals or the audio signal itself.
  • the electronic device 501-1 performs preprocessing on the audio signal (e.g., frequency band filtering, noise suppression, automatic gain control (AGC), acoustic echo cancelling (AEC), noise cancellation, and/or A voice signal can be obtained from an audio signal through windowing.
  • preprocessing e.g., frequency band filtering, noise suppression, automatic gain control (AGC), acoustic echo cancelling (AEC), noise cancellation, and/or
  • a voice signal can be obtained from an audio signal through windowing.
  • the electronic device 501-1 can identify voice characteristics from a voice signal.
  • the electronic device 501-1 may identify voice features (eg, at least one of speaker, age, gender, or language) using the speaker identification module 740.
  • the speaker identification module 740 can identify voice features corresponding to the voice signal by extracting a voice vector from the voice signal.
  • the speaker identification module 740 may identify at least one of the speaker, age, gender, or language of the voice included in the voice signal based on the voice vector.
  • the speaker identification module 740 can identify the speaker of a voice from a voice signal.
  • the speaker identification model DB 745 may include at least one speaker identification model (eg, speaker identification vector).
  • One speaker identification model may correspond to a previously generated speech vector based on the speech of one speaker.
  • the speaker identification module 740 converts the received voice signal into a voice vector and compares the voice vector with the speaker identification model stored in the speaker identification model DB 745 to identify the speaker. For example, if the similarity (e.g., likelihood ratio test (LRT), Euclidian distance, or Cosine similarity) between the speech vector and the speaker identification model is greater than or equal to the specified similarity, the speaker identification module 740 determines that the speaker in the speech signal is stored. It can be determined by corresponding to the speaker in the identification model.
  • LRT likelihood ratio test
  • the speaker identification module 740 may identify the speaker's age and/or gender from the voice signal. For example, the speaker identification module 740 may extract features of a voice signal and identify the speaker's age, gender, and/or language based on the pattern of features.
  • the speaker identification module 740 can extract linguistic features of the voice signal and identify the language of the voice included in the voice signal based on the feature pattern. In one example, language identification of the speech signal may be performed by translation module 760.
  • the ASR module 750 may convert a voice signal obtained by the microphone 650 into text.
  • the electronic device 505-1 may display text converted by the ASR module 750 on the display 660.
  • the electronic device 505-1 can convert voice signals in real time and display text.
  • EPD module 730 can detect the endpoint from the voice signal. For example, the EPD module 730 may detect the end point of the voice signal when a silence section exceeding the first threshold time is detected from the voice signal. In the present disclosure, a silent section may be referred to as a section in which no voice is detected from a voice signal. The EPD module 730 may detect a section in which the volume of the voice signal is less than or equal to a specified value as a silent section. The EPD module 730 may detect a section in which a signal component corresponding to a designated band (e.g., human voice band) is not detected from the voice signal as a silent section. When an endpoint is identified from a voice signal, the EPD module 730 may transmit information indicating that the endpoint has been identified to the ASR module 750.
  • a designated band e.g., human voice band
  • the electronic device 501-1 may maintain the microphone 650 in an activated state even if an endpoint is detected.
  • the electronic device 501-1 may maintain the microphone 650 in an activated state for at least a specified time (eg, a second threshold time) after detecting the endpoint.
  • the electronic device 501-1 may deactivate the microphone 650.
  • the electronic device 501-1 may deactivate the microphone 650 using the microphone control module 780.
  • the ASR module 750 may transmit the text converted from the voice signal to the translation module 760.
  • the converted text may be text corresponding to the voice acquired before endpoint detection.
  • the converted text may be text corresponding to a voice signal (eg, a first voice signal) from when the microphone 650 is activated until the end point is detected.
  • the electronic device 501-1 may acquire a subsequent voice signal (eg, a second voice signal) through the microphone 650.
  • a voice signal acquired subsequent to an endpoint may be referred to as a second voice signal
  • a voice signal acquired prior to an endpoint may be referred to as a first voice signal.
  • the ASR module 750 may display text corresponding to the second voice signal on the display 660.
  • the speaker identification module 740 may compare the first voice feature of the first voice signal and the second voice feature of the second voice signal. For example, speaker identification module 740 may compare the speaker, age, gender, and/or language of the first and second voice signals.
  • the speaker identification module 740 may determine that the second voice characteristic corresponds to the first voice characteristic if the similarity between the first voice characteristic and the second voice characteristic is greater than or equal to a preset value. If the similarity between the first voice feature and the second voice feature is less than a preset value, or if the difference exceeds a specified range, the speaker identification module 740 may determine that the first speaker and the second speaker are different speakers. .
  • the speaker identification module 740 may determine whether the first speaker of the first voice signal and the second speaker of the second voice signal are the same speaker. If the first speaker and the second speaker are determined to be the same speaker, the speaker identification module 740 may determine that the first voice feature and the second voice feature correspond. The speaker identification module 740 may identify the speaker corresponding to the first voice signal using the first voice signal and the speaker identification model stored in the speaker identification module DB 745. The speaker identification module 740 may attempt to identify the speaker corresponding to the second voice signal using the second voice signal and the speaker identification model stored in the speaker identification module DB 745. For example, if the first voice signal and the second voice signal correspond to the same speaker, the speaker identification module 740 may determine that the first voice feature and the second voice feature correspond. For example, when the first voice signal and the second voice signal correspond to different speakers or when speaker identification for the second voice signal fails, the speaker identification module 740 uses the first voice feature and the second voice feature. You can decide not to respond.
  • the speaker identification module 740 may determine that the first voice feature corresponds to the second voice feature if at least one of age, gender, or language of the first voice signal and the second voice signal is the same. .
  • the ASR module 750 may transmit the first text corresponding to the first voice signal to the translation module 760.
  • the translation module 760 may obtain the translated first text by translating the first text obtained from the ASR module 750 into the target language.
  • the translation module 760 may translate the text corresponding to the voice signal into the target language, for example, based on machine learning.
  • the translation methods in this disclosure are exemplary, and those skilled in the art will understand that any of a variety of translation methods may be used.
  • the execution module 770 may provide the translated first text through the display 660.
  • the execution module 770 may provide the translated first text through a speaker (not shown).
  • execution module 770 may cause microphone control module 780 to disable microphone 650 when providing translated first text. In this case, translation for the second text may not be provided. In one example, execution module 770 can display a button for translation of the second text on display 660.
  • the ASR module 750 sends a first text corresponding to the first speech signal and a second text corresponding to the second speech signal to the translation module 760.
  • 2 Text can be transmitted.
  • ASR module 750 may forward the first text and second text to translation module 760 once the endpoint for the second speech signal is identified.
  • the translation module 760 may obtain the translated first and second texts by translating the first and second texts obtained from the ASR module 750 into the target language.
  • the translation module 760 may obtain one translated sentence by combining the first text and the second text and translating it into the target language. there is.
  • the execution module 770 may provide the translated first and second texts through the display 660.
  • the execution module 770 may provide the translated first and second texts through a speaker (not shown).
  • execution module 770 may cause microphone control module 780 to disable microphone 650 when providing translated first and second texts.
  • the first voice feature and the second voice feature do not correspond, but the electronic device 505-1 may provide translated second text that corresponds to the second voice signal.
  • the first voice feature may correspond to the first speaker in the speaker identification model DB 745
  • the second voice feature may correspond to the second speaker in the speaker identification model DB 745.
  • the second voice signal is from a different speaker than the first voice signal
  • the speaker of the second voice signal may also correspond to a speaker previously stored in the electronic device 505-1.
  • the electronic device 505-1 may provide the translated second text.
  • translation of text may be performed by the server device 511 of FIG. 5 .
  • the electronic device 501-1 may transmit text corresponding to the voice signal to the server device using the communication circuit 690 and obtain translated text from the server device.
  • translation module 760 may be omitted.
  • FIG. 7B shows configurations of an electronic device and a peripheral electronic device according to an embodiment.
  • the electronic device 501-2 of FIG. 7B is an example of the electronic device 501 of FIG. 6 .
  • the electronic device 501-2 of FIG. 7B may be an electronic device that includes the configuration of the electronic device 501 described above with reference to FIG. 6 .
  • the description of the electronic device 501-2 described later in relation to FIG. 7B may be equally applied to the electronic device 501 of FIG. 6.
  • the description described with respect to the electronic device 501-1 in FIG. 7A may be applied to the electronic device 501-2 in FIG. 7B.
  • the electronic device 501-2 may obtain an audio signal or voice signal from the surrounding electronic device 521.
  • the electronic device 501-2 may be connected to the peripheral electronic device 521 using the communication circuit 690.
  • the peripheral electronic device 521 may include a microphone 651, a voice activity detection (VAD) module 710, a microphone control module 720, and a communication circuit 691.
  • the peripheral electronic device 521 may communicate with the electronic device 501-2 using the communication circuit 691.
  • the peripheral electronic device 521 may be a wearable electronic device that can be worn on the user's body, such as earbuds or AR glasses.
  • the peripheral electronic device 521 can detect whether the wearer utters a utterance using the VAD module 710.
  • the peripheral electronic device 521 can detect speech by the wearer by using an acceleration sensor to detect a change in acceleration according to the wearer's speech.
  • the peripheral electronic device 521 can inform whether the voice signal was uttered by the wearer.
  • the speaker identification module 740 may determine whether the first voice feature corresponds to the second voice feature based on information received from the peripheral electronic device 521.
  • the first voice signal may be from the wearer of the peripheral electronic device 521, but the second voice signal may not be from the wearer of the peripheral electronic device 521.
  • the electronic device 501-2 may determine that the speaker of the first voice signal and the speaker of the second voice signal are different based on information received from the peripheral electronic device 521.
  • the electronic device 501-2 determines whether the first voice feature of the first voice signal corresponds to the second voice feature of the second voice signal, and provides a translation based on the determination.
  • the execution module 770 may provide feedback (eg, auditory feedback) through the peripheral electronic device 521.
  • the electronic device 501-2 may determine control of the microphone 651 of the peripheral electronic device 521 using the microphone control module 780.
  • the microphone control module 720 of the peripheral electronic device 521 may control the microphone 651 based on the microphone control signal received from the electronic device 501-2.
  • FIGS. 8 to 16 various operations of the electronic device 501 may be described with reference to FIGS. 8 to 16 .
  • the following disclosure may be applied to the electronic device 501-1 in FIG. 7A and/or the electronic device 501-2 in FIG. 7B.
  • Those skilled in the art will understand that descriptions of various operations described below may change depending on the configuration of the electronic device 501-1 in FIG. 7A or the electronic device 501-2 in FIG. 7B.
  • Figure 8 shows an example of a conversation for which translation is provided.
  • a speaker 599 and the other speaker 598 may be having a conversation.
  • speaker 599 may be a user of electronic device 501.
  • the other speaker 598 may be a conversation partner whose speaker identification model is not stored in the electronic device 501.
  • the speaker 599 may utter the first speech 801.
  • the second time section T2 may be a silent section between the first speech 801 and the second speech 802.
  • the second time interval T2 is longer than the first threshold time TH1, but is shorter than the first threshold time TH1 and the second threshold time TH2.
  • the first threshold time (TH1) may be a threshold time for endpoint detection.
  • the electronic device 501 may detect the end point of the first speech 801 by detecting a silence period longer than the first threshold time TH1.
  • the electronic device 501 may maintain the activated state of the microphone 650 for the second threshold time TH2 even after detection of the endpoint.
  • the electronic device 501 may display the first text (e.g., automatic voice recognition result for the first speech) corresponding to the first speech 801 on the display 660.
  • the electronic device 501 may display the translated first text by translating the first text into the target language.
  • the electronic device 501 may acquire the second speech 802.
  • the electronic device 501 may display the second text (e.g., automatic voice recognition result for the second speech) corresponding to the second speech 802 on the display 660.
  • the electronic device 501 may determine whether the first voice feature of the first speech 801 of the electronic device 501 corresponds to the second voice feature of the second speech 802. For example, the electronic device 501 may determine whether the first voice feature and the second voice feature correspond according to the method described above with reference to FIG. 7A. In the example of FIG. 8, because both the first speech 801 and the second speech 802 were uttered by the speaker 599, the electronic device 501 determines that the first speech feature corresponds to the second speech feature. You can decide. In this case, the electronic device 501 may provide translations corresponding to the first text of the first speech 801 and the second text of the second speech 802. Electronic device 501 may provide translations visually and/or audibly. The electronic device 501 may additionally display a translated second text corresponding to the second text.
  • the fourth time section T4 may be a silent section between the second speech 802 and the third speech 803.
  • the fourth time interval T4 is longer than the first threshold time TH1, but is shorter than the first threshold time TH1 and the second threshold time TH2.
  • the electronic device 501 may detect the end point of the second speech 802 by detecting a silence period longer than the first threshold time TH1.
  • the electronic device 501 may provide a translation when the endpoint of the second speech 802 is detected.
  • the electronic device 501 may acquire the third speech 803 through the activated microphone 650.
  • the third speech 803 may be uttered by the other speaker 598.
  • the electronic device 501 may determine whether the second voice feature of the second speech 802 and the second voice feature of the third speech 803 correspond.
  • the electronic device 501 may determine whether the second voice feature and the third voice feature correspond according to the method described above with reference to FIG. 7A.
  • the electronic device 501 because the second speech 802 and the third speech 803 were uttered by different speakers, the electronic device 501 determines that the second speech feature does not correspond to the third speech feature. You can.
  • the electronic device 501 may provide a third text corresponding to the third speech 803.
  • the electronic device 501 may display a button for translating the third text together with the third text.
  • Figure 9 shows an example of a conversation for which translation is provided.
  • the other speaker 598 may perform the fourth speech 904 instead of the third speech 803 of FIG. 8 .
  • Descriptions of the first speech 801 and the second speech 802 may be referenced by the description related to FIG. 8.
  • the electronic device 501 may detect a silence period exceeding the first threshold time TH1 and the second threshold time TH2. As the second threshold time (TH2) is exceeded, the electronic device 501 may deactivate the microphone 650. Accordingly, the fourth speech 903 of the other speaker 598 in the fifth time interval T5 may not be received by the electronic device 501.
  • Figure 10 shows an example of a conversation for which translation is provided.
  • a fellow speaker 597 may speak instead of the other speaker 598 of FIG. 9.
  • the electronic device 501 may be assumed to have stored the speaker identification model of the fellow speaker 597.
  • Descriptions of the first speech 801 and the second speech 802 may be referenced by the description related to FIG. 8 .
  • the fourth time interval T4 is longer than the first threshold time TH1, but is shorter than the first threshold time TH1 and the second threshold time TH2.
  • the electronic device 501 may acquire the fifth speech 1003 through the activated microphone 650.
  • the fifth speech 1003 may be an utterance by a fellow speaker 597.
  • the electronic device 501 may determine whether the second voice feature of the second speech 802 corresponds to the fifth voice feature of the fifth speech 1003. For example, the electronic device 501 may determine whether the second voice feature and the fifth voice feature correspond according to the method described above with reference to FIG. 7A.
  • the electronic device 501 determines that the second speech feature does not correspond to the fifth speech feature. You can. In one example, the electronic device 501 may determine whether the fifth speech 1003 is uttered by the speaker for whom information is stored. If the fifth voice feature of the fifth speech 1003 corresponds to the speaker recognition model stored in the memory 630, the electronic device 501 may determine that the fifth speech 1003 is an utterance by the stored speaker. In this case, the electronic device 501 may visually and/or audibly provide a translation corresponding to the fifth speech 1003.
  • FIG. 11 illustrates a translation UI of an electronic device according to an embodiment.
  • the electronic device 501 may provide a translation UI according to execution of a translation application.
  • the first screen 1100-1 may correspond to an initial screen provided upon execution of a translation application.
  • the first screen 1100-1 may include a listening button 1110.
  • the electronic device 501 may enter the listening mode for at least a specified period of time.
  • the electronic device 501 may activate the microphone 650.
  • the electronic device 501 may display a second screen 1100-2 on the display 660 indicating that it is in listening mode.
  • the electronic device 501 may return to its initial state.
  • the electronic device 501 may output the first screen 1100-1 in the initial state.
  • FIG. 12 illustrates a translation UI of an electronic device according to an embodiment.
  • the electronic device 501 may display the third screen 1200-1 as part of the translation UI.
  • the third screen 1200-1 may be a screen output according to reception of the first speech 801 and the second speech 802 of FIGS. 8, 9, and 10.
  • the electronic device 501 may generate a first text 1210 corresponding to the first speech 801 and a translated first text 1210 corresponding to the first text 1210. Text 1220 can be displayed.
  • the electronic device 501 may display the second text 1230 corresponding to the second speech 802.
  • the electronic device 501 displays the second text 1230 and the translated first speech 802. 2 Text (1240) can be provided.
  • the electronic device 501 may display the translated text adjacent to the corresponding text.
  • the first text 1210 and the second text 1230 may constitute one sentence.
  • the electronic device 501 may provide a translation corresponding to the sentence composed of the first text 1210 and the second text 1230. The translation may be displayed below the second text 1230.
  • FIG. 13 illustrates a translation UI of an electronic device according to an embodiment.
  • the electronic device 501 may display the fifth screen 1300 as part of the translation UI.
  • the fifth screen 1300 may be a screen displayed by the electronic device 501 upon reception of the third speech 803 of FIG. 8.
  • the phonetic characteristics of the second speech 802 may not correspond to the phonetic characteristics of the third speech 803 .
  • the electronic device 501 may display the third text 1310 corresponding to the third speech 803.
  • the electronic device 501 may display a translation button 1320 along with the third text 1310.
  • the electronic device 501 may display the third text 1310, which has different voice characteristics, differently from the first text 1210 and the second text 1230.
  • the electronic device 501 may align the third text 1310 differently from the first text 1210 and the second text 1230.
  • the electronic device 501 may additionally display arbitrary graphics (eg, icons) to distinguish the speaker of the third text 1230.
  • the electronic device 501 may display the listening button 1110 again.
  • Figure 14 illustrates a translation UI of an electronic device according to an embodiment.
  • the electronic device 501 may display the sixth screen 1400 as part of the translation UI.
  • the electronic device 501 may display the translated third text 1410 for the third text 1310. For example, as in the example of FIG. 8, if the voice characteristics are different from the previous voice signal and the speaker of the voice signal is not the stored speaker, the electronic device 501 may display the fifth screen 1300 of FIG. 13. You can. When an input to the translation button 1320 of the fifth screen 1300 is received, the electronic device 501 may display the sixth screen 1400.
  • the third text 1310 may correspond to an utterance by a speaker stored in the electronic device 501 (e.g., fellow speaker 597 in FIG. 10 ).
  • the electronic device 501 can display the translated third text 1410 even if no input for a separate translation button is received.
  • the electronic device 501 may display speech-corresponding text so that speech by different speakers can be visually distinguished for speaker distinction. 12, 13, and 14, the alignment of the text is different depending on the speaker, but this is an example and the embodiments of the present disclosure are not limited thereto.
  • Figure 15 shows a flowchart of a method for providing translation on an electronic device according to an embodiment.
  • the electronic device 501 may be configured to provide translation based on speaker characteristics (e.g., at least one of voice vector, age, gender, or language). .
  • the electronic device 501 activates the microphone 650 and receives first audio.
  • the electronic device 501 may activate the microphone 650 when an input to the listening button 1110 of FIG. 11 is received.
  • the electronic device 501 may receive first audio using the activated microphone 650.
  • the electronic device 501 may determine whether the silent time period exceeds a first threshold time (eg, the first threshold time TH1 in FIG. 8). When a silent time period exceeding the first threshold time is detected, the electronic device 501 may identify the end point. For example, operation 1510 may correspond to the operation of EPD module 730 described above with respect to FIG. 7A. If a silent time period exceeding the first threshold time is not detected (e.g., operation 1510-NO), the electronic device 501 may continue to receive the first audio.
  • a first threshold time e.g., the first threshold time TH1 in FIG. 8
  • the electronic device 501 may display the first audio corresponding text and the translated first text.
  • the electronic device 501 may detect the end point of the first audio and display text corresponding to the first audio using the ASR module 750 described above with reference to FIG. 7A.
  • the electronic device 501 may obtain the translated first text using the translation module 760 described above with reference to FIG. 7A.
  • the electronic device 501 may transmit the first audio-corresponding text to the server device and receive the first audio-corresponding translated text from the server device using the communication circuit 690.
  • the electronic device 501 may determine whether the second audio is received within a second threshold time (eg, the second threshold time TH2 in FIG. 8). For example, when the first threshold time expires, the electronic device 501 may maintain the activated state of the microphone 650 for at least a second threshold time.
  • a second threshold time eg, the second threshold time TH2 in FIG. 8
  • the electronic device 501 may end the listening mode.
  • the electronic device 501 may determine whether the first speaker feature corresponds to the second speaker feature.
  • the first speaker feature may correspond to the speaker feature of the first audio
  • the second speaker feature may correspond to the speaker feature of the second audio.
  • the electronic device 501 may determine whether the first speaker feature corresponds to (eg, matches) the second speaker feature using the speaker identification module 740 of FIG. 7A.
  • Speaker characteristics may include at least one of voice vector, gender, age, or language.
  • the electronic device 501 may display the second audio corresponding text and the translated second text.
  • the translated second text may be a text obtained by translating the second audio corresponding text into the target language.
  • the electronic device 501 may determine whether the second speaker is the stored speaker. For example, when a speaker authentication model corresponding to a second speaker is stored, the electronic device 501 may determine that the second speaker is the stored speaker. If the second speaker is a stored speaker (e.g., operation 1535-YES), the electronic device 501 may display the translated text according to operation 1530.
  • the electronic device 501 may display the second text corresponding to the second audio according to operation 1540. In this case, the electronic device 501 may not display the translated text of the second audio corresponding text.
  • the electronic device 501 may display a button for translating the second text corresponding to the second audio (eg, the translation button 1320 of FIG. 13) along with the second text corresponding to the second audio.
  • Figure 16 shows a flowchart of a method for providing translation on an electronic device according to an embodiment.
  • the electronic device 501 may be set to provide translation based on voice characteristics (eg, at least one of speaker, age, gender, or language).
  • voice characteristics eg, at least one of speaker, age, gender, or language.
  • the electronic device 501 may activate the microphone 650 based on a first user input to activate voice translation. For example, the electronic device 501 may activate the microphone 650 when an input to the listening button 1110 of FIG. 11 is received.
  • the electronic device 501 may receive an audio signal through an activated microphone.
  • the electronic device 501 may detect the first endpoint from the first voice signal included in the audio signal.
  • the electronic device 501 may detect the first endpoint when a silent section exceeding a first threshold time (eg, the first threshold time TH1 in FIG. 8) is identified from the audio signal.
  • operation 1615 may correspond to the operation of EPD module 730 described above with respect to FIG. 7A.
  • the electronic device 501 may display the first text and the translated first text based on the voice recognition result for the first voice signal.
  • the first speech signal may be referenced to a previously acquired speech signal of the first endpoint.
  • the electronic device 501 may display the first text corresponding to the first voice signal using the ASR module 750 described above with reference to FIG. 7A.
  • the electronic device 501 may obtain the translated first text using the translation module 760 described above with reference to FIG. 7A.
  • the electronic device 501 may transmit the first audio corresponding text to the server device and receive the translated first text from the server device using the communication circuit 690.
  • the electronic device 501 may display the activation state of the microphone 650 for at least a first time period (eg, the second threshold time TH2 in FIG. 8). In one example, the electronic device 501 may deactivate the microphone 650 if a voice signal is not detected within the first time period.
  • a first time period eg, the second threshold time TH2 in FIG. 8
  • the electronic device 501 may deactivate the microphone 650 if a voice signal is not detected within the first time period.
  • the electronic device 501 may display the second text.
  • the first voice characteristic and the second voice characteristic can be compared.
  • the first voice characteristic may correspond to the voice characteristic of the first voice signal
  • the second voice characteristic may correspond to the voice characteristic of the second voice signal.
  • the electronic device 501 may determine whether the first voice characteristic and the second voice characteristic match.
  • the first voice characteristic may correspond to the voice characteristic of the first voice signal
  • the second voice characteristic may correspond to the voice characteristic of the second voice signal.
  • the electronic device 501 may determine whether the first voice characteristic matches (eg, corresponds to) the second voice characteristic using the speaker identification module 740 of FIG. 7A.
  • Voice characteristics may include at least one of a feature distribution pattern, gender, age, or language.
  • the electronic device 501 may identify a first similarity between a speaker identification model stored in memory 630 and a first speech characteristic, and identify a second similarity between a speaker identification model and the second speech characteristic. You can. If the difference between the first similarity and the second similarity is within a specified range, the electronic device 501 may determine that the first voice characteristic and the second voice characteristic are matched.
  • the electronic device 501 may display the second text and the translated second text in operation 1640.
  • the translated second text may be a text obtained by translating the first text and the second text into the target language.
  • the electronic device 501 may identify the second endpoint of the second voice signal when a silent section exceeding a first threshold time (e.g., the first threshold time (TH1) of FIG. 8) is identified from the audio signal. You can. Once the second endpoint is identified, the electronic device 501 can translate the first text and the second text into the target language.
  • a first threshold time e.g., the first threshold time (TH1) of FIG. 8
  • the electronic device 501 may display a second text corresponding to the second voice signal.
  • the electronic device 501 may display the second text without the translated second text.
  • the electronic device 501 may display an icon (eg, translation button 1320 in FIG. 13) for requesting translation of the second text along with the second text.
  • the electronic device 501 may display the translated second text by translating the second text into the target language along with the second text.
  • the electronic device 501 matches the second voice characteristic with the speaker recognition model stored in the memory 630. You can compare. If the second voice characteristic corresponds to the stored speaker recognition model, the electronic device 501 may display the second text and the translated second text by translating the second text into the target language.

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Abstract

Est divulgué un dispositif électronique comprenant un afficheur, une mémoire et un processeur. Le processeur peut déterminer si une première caractéristique vocale d'un premier signal vocal acquis avant un point d'extrémité correspond à une seconde caractéristique vocale d'un second signal vocal obtenu après le point d'extrémité. Lorsque la première caractéristique vocale et la seconde caractéristique vocale correspondent, le processeur peut fournir une traduction d'un premier texte correspondant au premier signal vocal et d'un second texte correspondant au second signal vocal. Lorsque la première caractéristique vocale et la seconde caractéristique vocale ne correspondent pas, le processeur peut fournir une traduction du premier texte correspondant au premier signal vocal.
PCT/KR2023/014738 2022-09-26 2023-09-26 Procédé de traduction basé sur une caractéristique vocale et dispositif électronique associé WO2024071946A1 (fr)

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KR20220121458 2022-09-26
KR10-2022-0121458 2022-09-26
KR10-2022-0127120 2022-10-05
KR1020220127120A KR20240043021A (ko) 2022-09-26 2022-10-05 음성 특성 기반 번역 방법 및 이를 위한 전자 장치

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KR20150093482A (ko) * 2014-02-07 2015-08-18 한국전자통신연구원 화자 분할 기반 다자간 자동 통번역 운용 시스템 및 방법과 이를 지원하는 장치
JP2015187738A (ja) * 2015-05-15 2015-10-29 株式会社東芝 音声翻訳装置、音声翻訳方法および音声翻訳プログラム
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