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    End of query detection
    2.
    发明授权
    End of query detection 有权

    公开(公告)号：US10593352B2

    公开(公告)日：2020-03-17

    申请号：US16001140

    申请日：2018-06-06

    Applicant: Google LLC

    Inventor： Gabor Simko ,  Maria Carolina Parada San Martin ,  Sean Matthew Shannon

    IPC: G10L17/00 ,  G10L25/78 ,  G10L15/22 ,  G10L15/187 ,  G10L15/065 ,  G10L15/18

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.

    End of query detection
    3.
    发明授权
    End of query detection 有权

    公开(公告)号：US11551709B2

    公开(公告)日：2023-01-10

    申请号：US16778222

    申请日：2020-01-31

    Applicant: Google LLC

    Inventor： Gabor Simko ,  Maria Carolina Parada San Martin ,  Sean Matthew Shannon

    IPC: G10L25/78 ,  G10L15/22 ,  G10L15/187 ,  G10L15/065 ,  G10L15/18

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for detecting an end of a query are disclosed. In one aspect, a method includes the actions of receiving audio data that corresponds to an utterance spoken by a user. The actions further include applying, to the audio data, an end of query model. The actions further include determining the confidence score that reflects a likelihood that the utterance is a complete utterance. The actions further include comparing the confidence score that reflects the likelihood that the utterance is a complete utterance to a confidence score threshold. The actions further include determining whether the utterance is likely complete or likely incomplete. The actions further include providing, for output, an instruction to (i) maintain a microphone that is receiving the utterance in an active state or (ii) deactivate the microphone that is receiving the utterance.

    Unified endpointer using multitask and multidomain learning
    4.
    发明授权
    Unified endpointer using multitask and multidomain learning 有权

    公开(公告)号：US10929754B2

    公开(公告)日：2021-02-23

    申请号：US16711172

    申请日：2019-12-11

    Applicant: Google LLC

    Inventor： Shuo-yiin Chang ,  Bo Li ,  Gabor Simko ,  Maria Carolina Parada San Martin ,  Sean Matthew Shannon

    IPC: G10L15/16 ,  G06N3/08 ,  G06N3/04 ,  G06N20/20 ,  G06K9/62 ,  G06N5/04

    Abstract: A method for training an endpointer model includes short-form speech utterances and long-form speech utterances. The method also includes providing a short-form speech utterance as input to a shared neural network, the shared neural network configured to learn shared hidden representations suitable for both voice activity detection (VAD) and end-of-query (EOQ) detection. The method also includes generating, using a VAD classifier, a sequence of predicted VAD labels and determining a VAD loss by comparing the sequence of predicted VAD labels to a corresponding sequence of reference VAD labels. The method also includes, generating, using an EOQ classifier, a sequence of predicted EOQ labels and determining an EOQ loss by comparing the sequence of predicted EOQ labels to a corresponding sequence of reference EOQ labels. The method also includes training, using a cross-entropy criterion, the endpointer model based on the VAD loss and the EOQ loss.

    Encoder-decoder models for sequence to sequence mapping
    5.
    发明授权
    Encoder-decoder models for sequence to sequence mapping 有权

    公开(公告)号：US10706840B2

    公开(公告)日：2020-07-07

    申请号：US15846634

    申请日：2017-12-19

    Applicant: Google LLC

    Inventor： Hasim Sak ,  Sean Matthew Shannon

    IPC: G10L15/14 ,  G10L15/02 ,  G10L15/183 ,  G10L15/06 ,  G10L15/16 ,  G06N3/02 ,  G06N3/08 ,  G10L15/22 ,  G06N3/04 ,  G06N7/00

    Abstract: Methods, systems, and apparatus for performing speech recognition. In some implementations, acoustic data representing an utterance is obtained. The acoustic data corresponds to time steps in a series of time steps. One or more computers process scores indicative of the acoustic data using a recurrent neural network to generate a sequence of outputs. The sequence of outputs indicates a likely output label from among a predetermined set of output labels. The predetermined set of output labels includes output labels that respectively correspond to different linguistic units and to a placeholder label that does not represent a classification of acoustic data. The recurrent neural network is configured to use an output label indicated for a previous time step to determine an output label for the current time step. The generated sequence of outputs is processed to generate a transcription of the utterance, and the transcription of the utterance is provided.

    Variational embedding capacity in expressive end-to-end speech synthesis
    6.
    发明授权
    Variational embedding capacity in expressive end-to-end speech synthesis 有权

    公开(公告)号：US12067969B2

    公开(公告)日：2024-08-20

    申请号：US18302764

    申请日：2023-04-18

    Applicant: Google LLC

    Inventor： Eric Dean Battenberg ,  Daisy Stanton ,  Russell John Wyatt Skerry-Ryan ,  Soroosh Mariooryad ,  David Teh-Hwa Kao ,  Thomas Edward Bagby ,  Sean Matthew Shannon

    IPC: G10L13/00 ,  G10L13/047 ,  G10L13/10

    CPC classification number: G10L13/047 ,  G10L13/10

    Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

    Variational embedding capacity in expressive end-to-end speech synthesis
    9.
    发明授权
    Variational embedding capacity in expressive end-to-end speech synthesis 有权

    公开(公告)号：US11222621B2

    公开(公告)日：2022-01-11

    申请号：US16879714

    申请日：2020-05-20

    Applicant: Google LLC

    Inventor： Eric Dean Battenberg ,  Daisy Stanton ,  Russell John Wyatt Skerry-Ryan ,  Soroosh Mariooryad ,  David Teh-hwa Kao ,  Thomas Edward Bagby ,  Sean Matthew Shannon

    IPC: G10L15/22 ,  G06N3/00 ,  G06N3/08 ,  G10L13/047 ,  G10L13/10

    Abstract: A method for estimating an embedding capacity includes receiving, at a deterministic reference encoder, a reference audio signal, and determining a reference embedding corresponding to the reference audio signal, the reference embedding having a corresponding embedding dimensionality. The method also includes measuring a first reconstruction loss as a function of the corresponding embedding dimensionality of the reference embedding and obtaining a variational embedding from a variational posterior. The variational embedding has a corresponding embedding dimensionality and a specified capacity. The method also includes measuring a second reconstruction loss as a function of the corresponding embedding dimensionality of the variational embedding and estimating a capacity of the reference embedding by comparing the first measured reconstruction loss for the reference embedding relative to the second measured reconstruction loss for the variational embedding having the specified capacity.

    Unified Endpointer Using Multitask and Multidomain Learning
    10.
    发明申请
    Unified Endpointer Using Multitask and Multidomain Learning 有权

    公开(公告)号：US20210142174A1

    公开(公告)日：2021-05-13

    申请号：US17152918

    申请日：2021-01-20

    Applicant: Google LLC

    Inventor： Shuo-yiin Chang ,  Bo Li ,  Gabor Simko ,  Maria Corolina Parada San Martin ,  Sean Matthew Shannon

    IPC: G06N3/08 ,  G06N3/04 ,  G10L15/16 ,  G06N20/20 ,  G06K9/62 ,  G06N5/04

    Abstract: A method for training an endpointer model includes short-form speech utterances and long-form speech utterances. The method also includes providing a short-form speech utterance as input to a shared neural network, the shared neural network configured to learn shared hidden representations suitable for both voice activity detection (VAD) and end-of-query (EOQ) detection. The method also includes generating, using a VAD classifier, a sequence of predicted VAD labels and determining a VAD loss by comparing the sequence of predicted VAD labels to a corresponding sequence of reference VAD labels. The method also includes, generating, using an EOQ classifier, a sequence of predicted EOQ labels and determining an EOQ loss by comparing the sequence of predicted EOQ labels to a corresponding sequence of reference EOQ labels. The method also includes training, using a cross-entropy criterion, the endpointer model based on the VAD loss and the EOQ loss.

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