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    Optimizing Inference Performance for Conformer
    1.
    发明申请
    Optimizing Inference Performance for Conformer 有权

    公开(公告)号：US20230130634A1

    公开(公告)日：2023-04-27

    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 ,  G10L15/22 ,  G10L15/06

    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.

    Fast Emit Low-latency Streaming ASR with Sequence-level Emission Regularization
    2.
    发明申请
    Fast Emit Low-latency Streaming ASR with Sequence-level Emission Regularization 有权

    公开(公告)号：US20220122586A1

    公开(公告)日：2022-04-21

    申请号：US17447285

    申请日：2021-09-09

    Applicant: Google LLC

    Inventor： Jiahui Yu ,  Chung-cheng Chiu ,  Bo Li ,  Shuo-yiin Chang ,  Tara Sainath ,  Wei Han ,  Anmol Gulati ,  Yanzhang He ,  Arun Narayanan ,  Yonghui Wu ,  Ruoming Pang

    IPC: G10L15/06 ,  G10L15/22 ,  G10L15/30 ,  G10L15/16

    Abstract: A computer-implemented method of training a streaming speech recognition model that includes receiving, as input to the streaming speech recognition model, a sequence of acoustic frames. The streaming speech recognition model is configured to learn an alignment probability between the sequence of acoustic frames and an output sequence of vocabulary tokens. The vocabulary tokens include a plurality of label tokens and a blank token. At each output step, the method includes determining a first probability of emitting one of the label tokens and determining a second probability of emitting the blank token. The method also includes generating the alignment probability at a sequence level based on the first probability and the second probability. The method also includes applying a tuning parameter to the alignment probability at the sequence level to maximize the first probability of emitting one of the label tokens.

    Optimizing inference performance for conformer
    3.
    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.

    Systems and Methods for Training Dual-Mode Machine-Learned Speech Recognition Models
    5.
    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.

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