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    Optimizing inference performance for conformer
    12.
    发明授权
    Optimizing inference performance for conformer 有权

    公开(公告)号：US12190869B2

    公开(公告)日：2025-01-07

    申请号：US17936547

    申请日：2022-09-29

    Applicant: Google LLC

    Inventor： Tara N. Sainath ,  Rami Botros ,  Anmol Gulati ,  Krzysztof Choromanski ,  Ruoming Pang ,  Trevor Strohman ,  Weiran Wang ,  Jiahui Yu

    IPC: G10L15/16 ,  G10L15/06 ,  G10L15/22

    Abstract: A computer-implemented method includes receiving a sequence of acoustic frames as input to an automatic speech recognition (ASR) model. Here, the ASR model includes a causal encoder and a decoder. The method also includes generating, by the causal encoder, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The method also includes generating, by the decoder, a first probability distribution over possible speech recognition hypotheses. Here, the causal encoder includes a stack of causal encoder layers each including a Recurrent Neural Network (RNN) Attention-Performer module that applies linear attention.

    EFFICIENT STREAMING NON-RECURRENT ON-DEVICE END-TO-END MODEL
    14.
    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.

    Emitting Word Timings with End-to-End Models
    15.
    发明公开
    Emitting Word Timings with End-to-End Models 审中-公开

    公开(公告)号：US20240321263A1

    公开(公告)日：2024-09-26

    申请号：US18680797

    申请日：2024-05-31

    Applicant: Google LLC

    Inventor： Tara N. Sainath ,  Basilio Garcia Castillo ,  David Rybach ,  Trevor Strohman ,  Ruoming Pang

    IPC: G10L15/06 ,  G10L25/30 ,  G10L25/78

    CPC classification number: G10L15/063 ,  G10L25/30 ,  G10L25/78

    Abstract: A method includes receiving a training example that includes audio data representing a spoken utterance and a ground truth transcription. For each word in the spoken utterance, the method also includes inserting a placeholder symbol before the respective word identifying a respective ground truth alignment for a beginning and an end of the respective word, determining a beginning word piece and an ending word piece, and generating a first constrained alignment for the beginning word piece and a second constrained alignment for the ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective word and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective word. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.

    Systems and Methods for Training Dual-Mode Machine-Learned Speech Recognition Models
    19.
    发明公开
    Systems and Methods for Training Dual-Mode Machine-Learned Speech Recognition Models 审中-公开

    公开(公告)号：US20230237993A1

    公开(公告)日：2023-07-27

    申请号：US18011571

    申请日：2021-10-01

    Applicant: Google LLC

    Inventor： Jiahui Yu ,  Ruoming Pang ,  Wei Han ,  Anmol Gulati ,  Chung-Cheng Chiu ,  Bo Li ,  Tara N. Sainath ,  Yonghui Hu

    IPC: G10L15/16 ,  G10L15/32 ,  G10L15/22

    CPC classification number: G10L15/16 ,  G10L15/32 ,  G10L15/22

    Abstract: Systems and methods of the present disclosure are directed to a computing system, including one or more processors and a machine-learned multi-mode speech recognition model configured to operate in a streaming recognition mode or a contextual recognition mode. The computing system can perform operations including obtaining speech data and a ground truth label and processing the speech data using the contextual recognition mode to obtain contextual prediction data. The operations can include evaluating a difference between the contextual prediction data and the ground truth label and processing the speech data using the streaming recognition mode to obtain streaming prediction data. The operations can include evaluating a difference between the streaming prediction data and the ground truth label and the contextual and streaming prediction data. The operations can include adjusting parameters of the speech recognition model.

    Emitting Word Timings with End-to-End Models
    20.
    发明公开
    Emitting Word Timings with End-to-End Models 审中-公开

    公开(公告)号：US20230206907A1

    公开(公告)日：2023-06-29

    申请号：US18167050

    申请日：2023-02-09

    Applicant: Google LLC

    Inventor： Tara N Sainath ,  Basilio Garcia Castillo ,  David Rybach ,  Trevor Strohman ,  Ruoming Pang

    IPC: G10L15/06 ,  G10L25/30 ,  G10L25/78

    CPC classification number: G10L15/063 ,  G10L25/30 ,  G10L25/78

    Abstract: A method includes receiving a training example that includes audio data representing a spoken utterance and a ground truth transcription. For each word in the spoken utterance, the method also includes inserting a placeholder symbol before the respective word identifying a respective ground truth alignment for a beginning and an end of the respective word, determining a beginning word piece and an ending word piece, and generating a first constrained alignment for the beginning word piece and a second constrained alignment for the ending word piece. The first constrained alignment is aligned with the ground truth alignment for the beginning of the respective word and the second constrained alignment is aligned with the ground truth alignment for the ending of the respective word. The method also includes constraining an attention head of a second pass decoder by applying the first and second constrained alignments.

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