Abstract:
Disclosed is an electronic apparatus. The electronic apparatus includes: a communication interface, a memory, and a processor connected to the memory and the communication interface, the processor configured to control the electronic apparatus to, based on receiving a speech related to a function of the electronic apparatus, obtain text information corresponding to the received speech, control the communication interface to transmit the obtained text information to a server including a first neural network model corresponding to the function, execute the function based on response information received from the server, and based on identifying that an update period of the first neural network model is greater than or equal to a first threshold period based on the information related to the function of the electronic apparatus, the electronic apparatus may receive the information about the first neural network model from the server and store the information in the memory.
Abstract:
Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.
Abstract:
The present disclosure relates to a communication method and system for converging a 5th-Generation (5G) communication system for supporting higher data rates beyond a 4th-Generation (4G) system with a technology for Internet of Things (IoT). The present disclosure may be applied to intelligent services based on the 5G communication technology and the IoT-related technology, such as smart home, smart building, smart city, smart car, connected car, health care, digital education, smart retail, security and safety services. The method includes transmitting a service authorization request message to a first network device, the first network drive including a proximity-based service (ProSe) function, and receiving a service authorization response message from the first network device. The first terminal is a relay terminal capable of performing a UE-to-network relay function.
Abstract:
Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.
Abstract:
A system and method for registering a new device for a voice assistant service. The method, performed by a server, of registering a new device for a voice assistant service includes: comparing functions of a pre-registered device with functions of the new device; identifying functions corresponding to the functions of the pre-registered device among the functions of the new device, based on the comparison; obtaining pre-registered utterance data related to at least some of the identified functions; generating action data for the new device based on the identified functions and the pre-registered utterance data.
Abstract:
Disclosed are an electronic device capable of efficiently performing speech recognition and natural language understanding and a method for controlling thereof. The electronic device includes: a microphone; a non-volatile memory configured to store virtual assistant model data comprising data that is classified according to a plurality of domains and data that is commonly used for the plurality of domains; a volatile memory; and a processor configured to: based on receiving, through the microphone, a trigger input to perform speech recognition for a user speech, initiate loading the virtual assistant model data from the non-volatile memory into the volatile memory, load, into the volatile memory, first data from among the data classified according to the plurality of domains and, while loading the first data into the volatile memory, load at least a part of the data commonly used for the plurality of domains into the volatile memory.
Abstract:
Disclosed is an electronic device. The electronic device comprises: a microphone comprising circuitry; a speaker comprising circuitry; and a processor electrically connected to the microphone and speaker, wherein the processor, when a first user's voice is input through the microphone, identifies a user who uttered the first user's voice and provides a first response sound, which is obtained by inputting the first user's voice to an artificial intelligence model learned through an artificial intelligence algorithm, through the speaker, and when a second user's voice is input through the microphone, identifies a user who uttered the second user's voice, and if the user who uttered the first user's voice is the same as the user who uttered the second user's voice, provides a second response sound, which is obtained by inputting the second user's voice and utterance history information to the artificial intelligence model, through the speaker. In particular, at least some of the methods of providing a response sound to a user's voice may use an artificial intelligence model learned in accordance with at least one of a machine learning, neural network, or deep learning algorithm.
Abstract:
An electronic apparatus is provided. The electronic apparatus includes a memory and a processor configured to control the electronic apparatus to: classify a plurality of input data into a plurality of types to store in the memory, determine at least one among the input data of the classified plurality of types based on a voice command being recognized among the input data, and provide response information corresponding to the voice command based on the input data of the determined type.
Abstract:
An electronic apparatus is provided. The electronic apparatus includes a microphone, a memory storing at least one instruction, and a processor connected to the microphone and the memory configured to control the electronic apparatus, and the processor, by executing the at least one instruction, may, based on receiving a user voice signal through the microphone, obtain a text corresponding to the user voice signal, identify a plurality of sentences included in the obtained text, identify a domain corresponding to each of the plurality of sentences among a plurality of domains, based on a similarity of a first sentence and a second sentence, among the plurality of sentences, having a same domain being greater than or equal to a threshold value, obtain a third sentence in which the first sentence and the second sentence are combined by using a first neural network model, and perform natural language understanding for the third sentence.
Abstract:
An electronic apparatus includes a communication interface; a memory configured to store at least one instruction; and a processor configured to execute the at least one instruction to: receive a text corresponding to a user utterance and information regarding a first external device; obtain a plurality of weights of a plurality of elements related to the first external device; identify a second external device for obtaining response information; control the communication interface to transmit the text corresponding to the user utterance to the second external device; receive first response information regarding the user utterance from the second external device; obtain second response information; and control the communication interface to transmit the second response information to the first external device.