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    Cascaded encoders for simplified streaming and non-streaming ASR
    1.
    发明授权
    Cascaded encoders for simplified streaming and non-streaming ASR 有权

    公开(公告)号：US12154581B2

    公开(公告)日：2024-11-26

    申请号：US17237021

    申请日：2021-04-21

    Applicant: Google LLC

    Inventor： Arun Narayanan ,  Tara Sainath ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Rohit Prabhavalkar ,  Jiahui Yu ,  Ehsan Variani ,  Trevor Strohman

    IPC: G10L19/16 ,  G06N3/08 ,  G10L15/00 ,  G10L15/16 ,  G10L15/32 ,  G10L25/30

    Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.

    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.

    Deliberation by Text-Only and Semi-Supervised Training
    5.
    发明公开
    Deliberation by Text-Only and Semi-Supervised Training 审中-公开

    公开(公告)号：US20230298563A1

    公开(公告)日：2023-09-21

    申请号：US18186157

    申请日：2023-03-18

    Applicant: Google LLC

    Inventor： Ke Hu ,  Tara N. Sainath ,  Yanzhang He ,  Rohit Prabhavalkar ,  Sepand Mavandadi ,  Weiran Wang ,  Trevor Strohman

    IPC: G10L13/08 ,  G10L15/16 ,  G10L15/06

    CPC classification number: G10L13/08 ,  G10L15/16 ,  G10L15/063

    Abstract: A method of text-only and semi-supervised training for deliberation includes receiving training data including unspoken textual utterances that are each not paired with any corresponding spoken utterance of non-synthetic speech, and training a deliberation model that includes a text encoder and a deliberation decoder on the unspoken textual utterances. The method also includes receiving, at the trained deliberation model, first-pass hypotheses and non-causal acoustic embeddings. The first-pass hypotheses is generated by a recurrent neural network-transducer (RNN-T) decoder for the non-causal acoustic embeddings encoded by a non-causal encoder. The method also includes encoding, using the text encoder, the first-pass hypotheses generated by the RNN-T decoder, and generating, using the deliberation decoder attending to both the first-pass hypotheses and the non-causal acoustic embeddings, second-pass hypotheses.

    Learning Word-Level Confidence for Subword End-To-End Automatic Speech Recognition
    6.
    发明申请
    Learning Word-Level Confidence for Subword End-To-End Automatic Speech Recognition 有权

    公开(公告)号：US20220270597A1

    公开(公告)日：2022-08-25

    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 ,  G10L25/30 ,  G06N3/08

    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.

    Cascaded Encoders for Simplified Streaming and Non-Streaming ASR
    7.
    发明申请
    Cascaded Encoders for Simplified Streaming and Non-Streaming ASR 有权

    公开(公告)号：US20220122622A1

    公开(公告)日：2022-04-21

    申请号：US17237021

    申请日：2021-04-21

    Applicant: Google LLC

    Inventor： Arun Narayanan ,  Tara Sainath ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Rohit Prabhavalkar ,  Jiahui Yu ,  Ehsan Variani ,  Trevor Strohman

    IPC: G10L19/16 ,  G10L15/00 ,  G10L25/30 ,  G06N3/08

    Abstract: An automated speech recognition (ASR) model includes a first encoder, a second encoder, and a decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The second encoder receives, as input, the first higher order feature representation generated by the first encoder at each of the plurality of output steps, and generates, at each of the plurality of output steps, a second higher order feature representation for a corresponding first higher order feature frame. The decoder receives, as input, the second higher order feature representation generated by the second encoder at each of the plurality of output steps, and generates, at each of the plurality of time steps, a first probability distribution over possible speech recognition hypotheses.

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