A method and apparatus for generating a user intention understanding satisfaction evaluation model, a method and apparatus for evaluating a user intention understanding satisfaction, an electronic device and a storage medium are provided, relating to intelligent voice recognition and knowledge graphs. The method for generating a user intention understanding satisfaction evaluation model is: acquiring a plurality of sets of intention understanding data, at least one set of which comprises a plurality of sequences corresponding to multi-round behaviors of an intelligent device in multi-round man-machine interactions; and learning the plurality of sets of intention understanding data through a first machine learning model, to obtain the user intention understanding satisfaction evaluation model after the learning, wherein the user intention understanding satisfaction evaluation model is configured to evaluate user intention understanding satisfactions of the intelligent device in the multi-round man-machine interactions according to the plurality of sequences corresponding to the multi-round man-machine interactions.

Systems and processes for providing user-specific acoustic models are provided. In accordance with one example, a method includes, at an electronic device having one or more processors, receiving a plurality of speech inputs, each of the speech inputs associated with a same user of the electronic device; providing each of the plurality of speech inputs to a user-independent acoustic model, the user-independent acoustic model providing a plurality of speech results based on the plurality of speech inputs; initiating a user-specific acoustic model on the electronic device; and adjusting the user-specific acoustic model based on the plurality of speech inputs and the plurality of speech results.

Systems and processes for providing user-specific acoustic models are provided. In accordance with one example, a method includes, at an electronic device having one or more processors, receiving a plurality of speech inputs, each of the speech inputs associated with a same user of the electronic device; providing each of the plurality of speech inputs to a user-independent acoustic model, the user-independent acoustic model providing a plurality of speech results based on the plurality of speech inputs; initiating a user-specific acoustic model on the electronic device; and adjusting the user-specific acoustic model based on the plurality of speech inputs and the plurality of speech results.

A smart bed body structure includes: a bed body, a control unit, a speech recognition unit and an audio device. The control unit is electrically connected with the speech recognition unit; the speech recognition unit is electrically connected with the audio device. The bed body includes: a load-bearing mechanism, at least one turnover mechanism and a base. The control unit and the audio device are arranged on the bottom face of the load-bearing mechanism. The load-bearing mechanism is arranged on the top face of the base, the turnover mechanisms being arranged between the load-bearing mechanism and the base, the load-bearing mechanism can be upwards turned over relative to the base by means of the turnover mechanisms. The control unit is electrically connected with the turnover mechanisms.

A method includes receiving audio data corresponding to an utterance spoken by a user that includes a command for a digital assistant to perform a long-standing operation, activating a set of one or more warm words associated with a respective action for controlling the long-standing operation, and associating the activated set of one or more warm words with only the user. While the digital assistant is performing the long-standing operation, the method includes receiving additional audio data corresponding to an additional utterance, identifying one of the warm words from the activated set of warm words, and performing speaker verification on the additional audio data. The method further includes performing the respective action associated with the identified one of the warm words for controlling the long-standing operation when the additional utterance was spoken by the same user that is associated with the activated set of one or more warm words.

A method includes receiving audio data corresponding to an utterance spoken by a user that includes a command for a digital assistant to perform a long-standing operation, activating a set of one or more warm words associated with a respective action for controlling the long-standing operation, and associating the activated set of one or more warm words with only the user. While the digital assistant is performing the long-standing operation, the method includes receiving additional audio data corresponding to an additional utterance, identifying one of the warm words from the activated set of warm words, and performing speaker verification on the additional audio data. The method further includes performing the respective action associated with the identified one of the warm words for controlling the long-standing operation when the additional utterance was spoken by the same user that is associated with the activated set of one or more warm words.

A transcription service may receive a request from a developer to build a custom speech-to-text model for a specific domain of speech. The custom speech-to-text model for the specific domain may replace a general speech-to-text model or add to a set of one or more speech-to-text models available for transcribing speech. The transcription service may receive a training data and instructions representing tasks. The transcription service may determine respective schedules for executing the instructions based at least in part on dependencies between the tasks. The transcription service may execute the instructions according to the respective schedules to train a speech-to-text model for a specific domain using the training data set. The transcription service may deploy the trained speech-to-text model as part of a network-accessible service for an end user to convert audio in the specific domain into texts.

A transcription service may receive a request from a developer to build a custom speech-to-text model for a specific domain of speech. The custom speech-to-text model for the specific domain may replace a general speech-to-text model or add to a set of one or more speech-to-text models available for transcribing speech. The transcription service may receive a training data and instructions representing tasks. The transcription service may determine respective schedules for executing the instructions based at least in part on dependencies between the tasks. The transcription service may execute the instructions according to the respective schedules to train a speech-to-text model for a specific domain using the training data set. The transcription service may deploy the trained speech-to-text model as part of a network-accessible service for an end user to convert audio in the specific domain into texts.

Devices and methods are provided for using an interactive wrapper for media content. A method may include identifying, by a first device, a software container including a call to action. The method may include identifying non-interactive advertising content. The method may include generating an interactive advertisement by adding the advertising content to the software container, and sending the interactive advertisement to a second device for presentation. The method may include receiving an indication of a user interaction with the interactive advertisement, and determining, based on the user interaction, an action. The method may include sending data associated with the action to the second device or a third device for presentation.

Devices and methods are provided for using an interactive wrapper for media content. A method may include identifying, by a first device, a software container including a call to action. The method may include identifying non-interactive advertising content. The method may include generating an interactive advertisement by adding the advertising content to the software container, and sending the interactive advertisement to a second device for presentation. The method may include receiving an indication of a user interaction with the interactive advertisement, and determining, based on the user interaction, an action. The method may include sending data associated with the action to the second device or a third device for presentation.