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    Learning word-level confidence for subword end-to-end automatic speech recognition
    2.
    发明授权
    Learning word-level confidence for subword end-to-end automatic speech recognition 有权

    公开(公告)号：US11610586B2

    公开(公告)日：2023-03-21

    申请号：US17182592

    申请日：2021-02-23

    Applicant: Google LLC

    Inventor： David Qiu ,  Qiujia Li ,  Yanzhang He ,  Yu Zhang ,  Bo Li ,  Liangliang Cao ,  Rohit Prabhavalkar ,  Deepti Bhatia ,  Wei Li ,  Ke Hu ,  Tara Sainath ,  Ian Mcgraw

    IPC: G10L15/22 ,  G10L15/08 ,  G06N3/08 ,  G10L25/30

    Abstract: A method includes receiving a speech recognition result, and using a confidence estimation module (CEM), for each sub-word unit in a sequence of hypothesized sub-word units for the speech recognition result: obtaining a respective confidence embedding that represents a set of confidence features; generating, using a first attention mechanism, a confidence feature vector; generating, using a second attention mechanism, an acoustic context vector; and generating, as output from an output layer of the CEM, a respective confidence output score for each corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the CEM. For each of the one or more words formed by the sequence of hypothesized sub-word units, the method also includes determining a respective word-level confidence score for the word. The method also includes determining an utterance-level confidence score by aggregating the word-level confidence scores.

    Multi-Task Learning for End-To-End Automated Speech Recognition Confidence and Deletion Estimation
    3.
    发明申请
    Multi-Task Learning for End-To-End Automated Speech Recognition Confidence and Deletion Estimation 有权

    公开(公告)号：US20220310080A1

    公开(公告)日：2022-09-29

    申请号：US17643826

    申请日：2021-12-11

    Applicant: Google LLC

    Inventor： David Qiu ,  Yanzhang He ,  Yu Zhang ,  Qiujia Li ,  Liangliang Cao ,  Ian McGraw

    IPC: G10L15/197 ,  G10L15/06 ,  G10L15/22 ,  G10L15/02 ,  G10L15/16 ,  G10L15/30 ,  G10L15/32 ,  G10L15/04 ,  G06N3/08

    Abstract: A method including receiving a speech recognition result corresponding to a transcription of an utterance spoken by a user. For each sub-word unit in a sequence of hypothesized sub-word units of the speech recognition result, using a confidence estimation module to: obtain a respective confidence embedding associated with the corresponding output step when the corresponding sub-word unit was output from the first speech recognizer; generate a confidence feature vector; generate an acoustic context vector; and generate a respective confidence output score for the corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the confidence estimation module. The method also includes determining, based on the respective confidence output score generated for each sub-word unit in the sequence of hypothesized sub-word units, an utterance-level confidence score for the transcription of the utterance.

    KEY PHRASE SPOTTING
    5.
    发明申请
    KEY PHRASE SPOTTING 有权

    公开(公告)号：US20220199084A1

    公开(公告)日：2022-06-23

    申请号：US17654195

    申请日：2022-03-09

    Applicant: Google LLC

    Inventor： Wei Li ,  Rohit Prakash Prabhavalkar ,  Kanury Kanishka Rao ,  Yanzhang He ,  Ian C. McGraw ,  Anton Bakhtin

    IPC: G10L15/22 ,  G10L15/06 ,  G10L15/18 ,  G10L19/00 ,  G10L15/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for detecting utterances of a key phrase in an audio signal. One of the methods includes receiving, by a key phrase spotting system, an audio signal encoding one or more utterances; while continuing to receive the audio signal, generating, by the key phrase spotting system, an attention output using an attention mechanism that is configured to compute the attention output based on a series of encodings generated by an encoder comprising one or more neural network layers, generating, by the key phrase spotting system and using attention output, output that indicates whether the audio signal likely encodes the key phrase; and providing, by the key phrase spotting system, the output that indicates whether the audio signal likely encodes the key phrase.

    EFFICIENT STREAMING NON-RECURRENT ON-DEVICE END-TO-END MODEL
    6.
    发明申请
    EFFICIENT STREAMING NON-RECURRENT ON-DEVICE END-TO-END MODEL 有权

    公开(公告)号：US20240371363A1

    公开(公告)日：2024-11-07

    申请号：US18772263

    申请日：2024-07-15

    Applicant: Google LLC

    Inventor： Tara Sainath ,  Arun Narayanan ,  Rami Botros ,  Yanzhang He ,  Ehsan Variani ,  Cyril Allauzen ,  David Rybach ,  Ruoming Pang ,  Trevor Strohman

    IPC: G10L15/06 ,  G10L15/02 ,  G10L15/22 ,  G10L15/30

    Abstract: An ASR model includes a first encoder configured to receive a sequence of acoustic frames and generate a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The ASR model also includes a second encoder configured to receive the first higher order feature representation generated by the first encoder at each of the plurality of output steps and generate a second higher order feature representation for a corresponding first higher order feature frame. The ASR model also includes a decoder configured to receive the second higher order feature representation generated by the second encoder at each of the plurality of output steps and generate a first probability distribution over possible speech recognition hypothesis. The ASR model also includes a language model configured to receive the first probability distribution over possible speech hypothesis and generate a rescored probability distribution.

    VOICE SHORTCUT DETECTION WITH SPEAKER VERIFICATION
    7.
    发明公开
    VOICE SHORTCUT DETECTION WITH SPEAKER VERIFICATION 审中-公开

    公开(公告)号：US20240363122A1

    公开(公告)日：2024-10-31

    申请号：US18765108

    申请日：2024-07-05

    Applicant: GOOGLE LLC

    Inventor： Rajeev Rikhye ,  Quan Wang ,  Yanzhang He ,  Qiao Liang ,  Ian C. McGraw

    IPC: G10L17/24 ,  G10L15/26 ,  G10L17/06 ,  G10L21/028

    CPC classification number: G10L17/24 ,  G10L15/26 ,  G10L17/06 ,  G10L21/028

    Abstract: Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance. Additionally or alternatively, the text representation of the utterance can be processed to determine whether at least a portion of the text representation of the utterance captures a particular keyphrase. When the system determines the registered and/or verified user spoke the utterance and the system determines the text representation of the utterance captures the particular keyphrase, the system can cause a computing device to perform one or more actions corresponding to the particular keyphrase.

    Voice shortcut detection with speaker verification
    10.
    发明授权
    Voice shortcut detection with speaker verification 有权

    公开(公告)号：US12033641B2

    公开(公告)日：2024-07-09

    申请号：US18103324

    申请日：2023-01-30

    Applicant: Google LLC

    Inventor： Rajeev Rikhye ,  Quan Wang ,  Yanzhang He ,  Qiao Liang ,  Ian C. McGraw

    IPC: G10L17/24 ,  G10L15/26 ,  G10L17/06 ,  G10L21/028

    CPC classification number: G10L17/24 ,  G10L15/26 ,  G10L17/06 ,  G10L21/028

    Abstract: Techniques disclosed herein are directed towards streaming keyphrase detection which can be customized to detect one or more particular keyphrases, without requiring retraining of any model(s) for those particular keyphrase(s). Many implementations include processing audio data using a speaker separation model to generate separated audio data which isolates an utterance spoken by a human speaker from one or more additional sounds not spoken by the human speaker, and processing the separated audio data using a text independent speaker identification model to determine whether a verified and/or registered user spoke a spoken utterance captured in the audio data. Various implementations include processing the audio data and/or the separated audio data using an automatic speech recognition model to generate a text representation of the utterance. Additionally or alternatively, the text representation of the utterance can be processed to determine whether at least a portion of the text representation of the utterance captures a particular keyphrase. When the system determines the registered and/or verified user spoke the utterance and the system determines the text representation of the utterance captures the particular keyphrase, the system can cause a computing device to perform one or more actions corresponding to the particular keyphrase.

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