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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.

    TWO-PASS END TO END SPEECH RECOGNITION
    7.
    发明申请
    TWO-PASS END TO END SPEECH RECOGNITION 有权

    公开(公告)号：US20240420687A1

    公开(公告)日：2024-12-19

    申请号：US18815537

    申请日：2024-08-26

    Applicant: GOOGLE LLC

    Inventor： Tara N. Sainath ,  Yanzhang He ,  Bo Li ,  Arun Narayanan ,  Ruoming Pang ,  Antoine Jean Bruguier ,  Shuo-yiin Chang ,  Wei Li

    IPC: G10L15/16 ,  G06N3/08 ,  G10L15/05 ,  G10L15/06 ,  G10L15/22

    Abstract: Two-pass automatic speech recognition (ASR) models can be used to perform streaming on-device ASR to generate a text representation of an utterance captured in audio data. Various implementations include a first-pass portion of the ASR model used to generate streaming candidate recognition(s) of an utterance captured in audio data. For example, the first-pass portion can include a recurrent neural network transformer (RNN-T) decoder. Various implementations include a second-pass portion of the ASR model used to revise the streaming candidate recognition(s) of the utterance and generate a text representation of the utterance. For example, the second-pass portion can include a listen attend spell (LAS) decoder. Various implementations include a shared encoder shared between the RNN-T decoder and the LAS decoder.

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