Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.

Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.

Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.

Abstract: Techniques disclosed herein enable training and/or utilizing speaker dependent (SD) speech models which are personalizable to any user of a client device. Various implementations include personalizing a SD speech model for a target user by processing, using the SD speech model, a speaker embedding corresponding to the target user along with an instance of audio data. The SD speech model can be personalized for an additional target user by processing, using the SD speech model, an additional speaker embedding, corresponding to the additional target user, along with another instance of audio data. Additional or alternative implementations include training the SD speech model based on a speaker independent speech model using teacher student learning.

Abstract: An apparatus to personalize voice recognition on a client device includes a microphone, an embedded speech recognizer, a tag comparator, a client query manager, a user interface and a tag generator. An embedded speech recognizer receives an audio input from a user and generates recognition candidates, selecting one recognition candidate from the generated candidates. A tag comparator compares the audio stream with a first stored audio tag. The client query manager receives the selected recognition candidate and if the tag comparator matches the audio stream with the first audio tag then the client query manager executes an associated query. If no tag match is found, then the client query manager executes a query using the selected recognition candidate. After an indication from the user of a selected result, a tag generator stores a second audio tag in the storage based on the selected recognition candidate and the selected result.

Abstract: A method, computer program product, and system are provided for performing a voice command on a client device. The method can include translating, using a first speech recognizer located on the client device, an audio stream of a voice command to a first machine-readable voice command and generating a first query result using the first machine-readable voice command to query a client database. In addition, the audio stream can be transmitted to a remote server device that translates the audio stream to a second machine-readable voice command using a second speech recognizer. Further, the method can include receiving a second query result from the remote server device, where the second query result is generated by the remote server device using the second machine-readable voice command and displaying the first query result and the second query result on the client device.

Abstract: A method, computer program product, and system are provided for performing a voice command on a client device. The method can include translating, using a first speech recognizer located on the client device, an audio stream of a voice command to a first machine-readable voice command and generating a first query result using the first machine-readable voice command to query a client database. In addition, the audio stream can be transmitted to a remote server device that translates the audio stream to a second machine-readable voice command using a second speech recognizer. Further, the method can include receiving a second query result from the remote server device, where the second query result is generated by the remote server device using the second machine-readable voice command and displaying the first query result and the second query result on the client device.

Abstract: A method, computer program product, and system are provided for performing a voice command on a client device. The method can include translating, using a first speech recognizer located on the client device, an audio stream of a voice command to a first machine-readable voice command and generating a first query result using the first machine-readable voice command to query a client database. In addition, the audio stream can be transmitted to a remote server device that translates the audio stream to a second machine-readable voice command using a second speech recognizer. Further, the method can include receiving a second query result from the remote server device, where the second query result is generated by the remote server device using the second machine-readable voice command and displaying the first query result and the second query result on the client device.

Abstract: A method, computer program product, and system are provided for performing a voice command on a client device. The method can include translating, using a first speech recognizer located on the client device, an audio stream of a voice command to a first machine-readable voice command and generating a first query result using the first machine-readable voice command to query a client database. In addition, the audio stream can be transmitted to a remote server device that translates the audio stream to a second machine-readable voice command using a second speech recognizer. Further, the method can include receiving a second query result from the remote server device, where the second query result is generated by the remote server device using the second machine-readable voice command and displaying the first query result and the second query result on the client device.

Abstract: A method, computer program product, and system are provided for performing a voice command on a client device. The method can include translating, using a first speech recognizer located on the client device, an audio stream of a voice command to a first machine-readable voice command and generating a first query result using the first machine-readable voice command to query a client database. In addition, the audio stream can be transmitted to a remote server device that translates the audio stream to a second machine-readable voice command using a second speech recognizer. Further, the method can include receiving a second query result from the remote server device, where the second query result is generated by the remote server device using the second machine-readable voice command and displaying the first query result and the second query result on the client device.

Abstract: A system and method to interactively converse with a cognitively overloaded user of a device, includes maintaining a knowledge base of information regarding the device and a domain, organizing the information in at least one of a relational manner and an ontological manner, receiving speech from the user, converting the speech into a word sequence, recognizing a partial proper name in the word sequence, identifying meaning structures from the word sequence using a model of the domain information, adjusting a boundary of the partial proper names to enhance an accuracy of the meaning structures, interpreting the meaning structures in a context of the conversation with the cognitively overloaded user using the knowledge base, selecting a content for a response to the cognitively overloaded user, generating the response based on the selected content, the context of the conversation, and grammatical rules, and synthesizing speech wave forms for the response.

Abstract: A system and method to interactively converse with a cognitively overloaded user of a device, includes maintaining a knowledge base of information regarding the device and a domain, organizing the information in at least one of a relational manner and an ontological manner, receiving speech from the user, converting the speech into a word sequence, recognizing a partial proper name in the word sequence, identifying meaning structures from the word sequence using a model of the domain information, adjusting a boundary of the partial proper names to enhance an accuracy of the meaning structures, interpreting the meaning structures in a context of the conversation with the cognitively overloaded user using the knowledge base, selecting a content for a response to the cognitively overloaded user, generating the response based on the selected content, the context of the conversation, and grammatical rules, and synthesizing speech wave forms for the response.