Abstract: A conversation interaction method and apparatus, and a computer-readable storage medium are provided. The method includes: converting a speech to be recognized into a first text; inputting the first text into a semantic analysis model, to obtain intention information and slot information of the first text; and inputting the intention information and the slot information of the first text into a conversation state machine, to obtain interaction information corresponding to the first text. By using a semantic analysis model, intention information and slot information of a first text are obtained directly from the first text. The process in the existing technology, where a semantic analysis model needs to be used immediately after a language model, is avoided, thereby shortening processing time and making it possible to respond faster to a user. Further, by using the above scheme, calculation complexity and the cost of a whole system are reduced.

Abstract: The present disclosure discloses a method and an apparatus for testing a speaker, an electronic device and a storage medium. A specific implementation includes: obtaining first audio data recorded by a microphone integrated with the speaker in ambient white noise; analyzing the first audio data to derive a first analysis result; and determining whether there is a defect in the microphone according to the first analysis result. Hence, these allow for testing a completed set on an assembled speaker to ensure the consistency of a microphone test and improve the accuracy of the test result.

Abstract: The present disclosure discloses a method and an apparatus for testing a speaker, an electronic device and a storage medium. A specific implementation includes: obtaining first audio data recorded by a microphone integrated with the speaker in ambient white noise; analyzing the first audio data to derive a first analysis result; and determining whether there is a defect in the microphone according to the first analysis result. Hence, these allow for testing a completed set on an assembled speaker to ensure the consistency of a microphone test and improve the accuracy of the test result.

Abstract: The present disclosure discloses a method and an apparatus for testing a speaker, an electronic device and a storage medium. A specific implementation includes: obtaining first audio data recorded by a microphone integrated with the speaker in ambient white noise; analyzing the first audio data to derive a first analysis result; and determining whether there is a defect in the microphone according to the first analysis result. Hence, these allow for testing a completed set on an assembled speaker to ensure the consistency of a microphone test and improve the accuracy of the test result.

Abstract: An apparatus, system, and method is disclosed for a hybrid approach to using AI agents and human agents to provide behavioral coaching. Hybrid modes of coaching are supported in which conversations can be handed off from AI agents to human agents. In some implementations, collaborate modes of coaching are supported in which a human agent collaborates with an AI agent.

Abstract: A conversation interaction method and apparatus, and a computer-readable storage medium are provided. The method includes: converting a speech to be recognized into a first text; inputting the first text into a semantic analysis model, to obtain intention information and slot information of the first text; and inputting the intention information and the slot information of the first text into a conversation state machine, to obtain interaction information corresponding to the first text. By using a semantic analysis model, intention information and slot information of a first text are obtained directly from the first text. The process in the existing technology, where a semantic analysis model needs to be used immediately after a language model, is avoided, thereby shortening processing time and making it possible to respond faster to a user. Further, by using the above scheme, calculation complexity and the cost of a whole system are reduced.

Abstract: Embodiments of the present disclosure provide a method and apparatus for communication authentication processing, and an electronic device, where the method includes: transmitting, by a first device, a pairing request to a second device; receiving, by the first device, a pairing response transmitted by the second device, where the pairing response includes a first random value and first signature information, the first random value is configured to generate the first signature information; and acquiring, by the first device, a second random value and second signature information from a server according to the first random value and the first signature information, where the second random value and the second signature information are generated by the server according to the first random value and the first signature information, the second random value is configured to generate the second signature information.

Abstract: Embodiments of the present disclosure provide a communication method, a communication apparatus and an electronic device. The method includes: a first device establishes a Bluetooth connection with a second device; the first device performs an authentication processing with the second device to obtain an authentication result; and the first device performs a voice-based data interaction with the second device if the authentication result is that an authentication is successful. The method can improve the connection speed, connection success rate and communication security during the communication of the device.

Abstract: Embodiments of the inventive system and methods are directed to a computer program that employs a drag-and-drop user interface for managing dialogue states, tracking dialogue context, understanding dialogue utterances, and managing dialogue sessions. Each dialogue element is defined in a “node” that can be dragged and dropped into a canvas of the user interface. An embodiment provides wiring mechanisms to freely link nodes. Dialogue utterances are contained in messages that flow through the wires linking different nodes until exiting the canvas to an end user. An executable image of a conversational agent is then generated by compiling the source code associated with the nodes based on their connections. A conversational agent can be deployed in an electronic device such as a home device, which configured to perform an action in response to a user verbal command or request using the conversational agent deployed therein.

Abstract: This disclosure provides a method, apparatus, device, and storage medium for voice interaction, where the method is applied to an AI device to determine whether a current scenario of the AI device is a preset scenario and waken a voice interaction function of the AI device to facilitate voice interaction with a user in response to the current scenario of the AI device being the preset scenario. A scenario directly triggers the voice interaction process, thereby avoiding the process of wakening by physical wakening or a wakening word, simplifying the process of using voice interaction, reducing the costs of learning voice interaction, and improving user experience.

Abstract: A method for selectively interacting with multi-devices is provided. The method includes the following steps: receiving identical voice information transmitted by a plurality of terminal devices respectively; performing voice recognition on the received voice information; calculating energy of a wake-up word in respective voice information; and comparing the energy of one wake-up word with another, and transmitting feedback information to the terminal devices according to an energy comparison result and a voice recognition result. By calculating the energy of the wake-up word in respective voice information transmitted by respective devices, the distances between respective device and a user can be distinguished. A unique response can be ensured by determining that the device closest to the user responds to the user's request, thus ensuring the user experience.

Abstract: This disclosure provides a method, apparatus, device, and storage medium for voice interaction, where the method is applied to an AI device to determine whether a current scenario of the AI device is a preset scenario and waken a voice interaction function of the AI device to facilitate voice interaction with a user in response to the current scenario of the AI device being the preset scenario. A scenario directly triggers the voice interaction process, thereby avoiding the process of wakening by physical wakening or a wakening word, simplifying the process of using voice interaction, reducing the costs of learning voice interaction, and improving user experience.

Abstract: Embodiments of the inventive system and methods are directed to a computer program that employs a drag-and-drop user interface for managing dialogue states, tracking dialogue context, understanding dialogue utterances, and managing dialogue sessions. Each dialogue element is defined in a “node” that can be dragged and dropped into a canvas of the user interface. An embodiment provides wiring mechanisms to freely link nodes. Dialogue utterances are contained in messages that flow through the wires linking different nodes until exiting the canvas to an end user. An executable image of a conversational agent is then generated by compiling the source code associated with the nodes based on their connections. A conversational agent can be deployed in an electronic device such as a home device, which configured to perform an action in response to a user verbal command or request using the conversational agent deployed therein.

Abstract: A method for selectively interacting with multi-devices is provided. The method includes the following steps: receiving identical voice information transmitted by a plurality of terminal devices respectively; performing voice recognition on the received voice information; calculating energy of a wake-up word in respective voice information; and comparing the energy of one wake-up word with another, and transmitting feedback information to the terminal devices according to an energy comparison result and a voice recognition result. By calculating the energy of the wake-up word in respective voice information transmitted by respective devices, the distances between respective device and a user can be distinguished. A unique response can be ensured by determining that the device closest to the user responds to the user's request, thus ensuring the user experience.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing keywords using a long short term memory neural network. One of the methods includes receiving, by a device for each of multiple variable length enrollment audio signals, a respective plurality of enrollment feature vectors that represent features of the respective variable length enrollment audio signal, processing each of the plurality of enrollment feature vectors using a long short term memory (LSTM) neural network to generate a respective enrollment LSTM output vector for each enrollment feature vector, and generating, for the respective variable length enrollment audio signal, a template fixed length representation for use in determining whether another audio signal encodes another spoken utterance of the enrollment phrase by combining at most a quantity k of the enrollment LSTM output vectors for the enrollment audio signal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a deep neural network. One of the methods includes training a deep neural network with a first training set by adjusting values for each of a plurality of weights included in the neural network, and training the deep neural network to determine a probability that data received by the deep neural network has features similar to key features of one or more keywords or key phrases, the training comprising providing the deep neural network with a second training set and adjusting the values for a first subset of the plurality of weights, wherein the second training set includes data representing the key features of the one or more keywords or key phrases.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing keywords using a long short term memory neural network. One of the methods includes receiving, by a device for each of multiple variable length enrollment audio signals, a respective plurality of enrollment feature vectors that represent features of the respective variable length enrollment audio signal, processing each of the plurality of enrollment feature vectors using a long short term memory (LSTM) neural network to generate a respective enrollment LSTM output vector for each enrollment feature vector, and generating, for the respective variable length enrollment audio signal, a template fixed length representation for use in determining whether another audio signal encodes another spoken utterance of the enrollment phrase by combining at most a quantity k of the enrollment LSTM output vectors for the enrollment audio signal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing keywords using a long short term memory neural network. One of the methods includes receiving, by a device for each of multiple variable length enrollment audio signals, a respective plurality of enrollment feature vectors that represent features of the respective variable length enrollment audio signal, processing each of the plurality of enrollment feature vectors using a long short term memory (LSTM) neural network to generate a respective enrollment LSTM output vector for each enrollment feature vector, and generating, for the respective variable length enrollment audio signal, a template fixed length representation for use in determining whether another audio signal encodes another spoken utterance of the enrollment phrase by combining at most a quantity k of the enrollment LSTM output vectors for the enrollment audio signal.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for recognizing keywords using a long short term memory neural network. One of the methods includes receiving, by a device for each of multiple variable length enrollment audio signals, a respective plurality of enrollment feature vectors that represent features of the respective variable length enrollment audio signal, processing each of the plurality of enrollment feature vectors using a long short term memory (LSTM) neural network to generate a respective enrollment LSTM output vector for each enrollment feature vector, and generating, for the respective variable length enrollment audio signal, a template fixed length representation for use in determining whether another audio signal encodes another spoken utterance of the enrollment phrase by combining at most a quantity k of the enrollment LSTM output vectors for the enrollment audio signal.