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    Speech recognition using neural networks
    1.
    发明授权
    Speech recognition using neural networks 有权

    公开(公告)号：US12243515B2

    公开(公告)日：2025-03-04

    申请号：US18177717

    申请日：2023-03-02

    Applicant: Google LLC

    Inventor： Andrew W. Senior ,  Ignacio L. Moreno

    IPC: G10L15/02 ,  G06N3/02 ,  G10L15/16

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech recognition using neural networks. A feature vector that models audio characteristics of a portion of an utterance is received. Data indicative of latent variables of multivariate factor analysis is received. The feature vector and the data indicative of the latent variables is provided as input to a neural network. A candidate transcription for the utterance is determined based on at least an output of the neural network.

    Setting latency constraints for acoustic models
    2.
    发明授权
    Setting latency constraints for acoustic models 有权

    公开(公告)号：US11996088B2

    公开(公告)日：2024-05-28

    申请号：US16918669

    申请日：2020-07-01

    Applicant: Google LLC

    Inventor： Andrew W. Senior ,  Hasim Sak ,  Kanury Kanishka Rao

    IPC: G10L15/16 ,  G06N3/044 ,  G06N3/045

    CPC classification number: G10L15/16 ,  G06N3/044 ,  G06N3/045

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for acoustic modeling of audio data. One method includes receiving audio data representing a portion of an utterance, providing the audio data to a trained recurrent neural network that has been trained to indicate the occurrence of a phone at any of multiple time frames within a maximum delay of receiving audio data corresponding to the phone, receiving, within the predetermined maximum delay of providing the audio data to the trained recurrent neural network, output of the trained neural network indicating a phone corresponding to the provided audio data using output of the trained neural network to determine a transcription for the utterance, and providing the transcription for the utterance.

    ASYNCHRONOUS OPTIMIZATION FOR SEQUENCE TRAINING OF NEURAL NETWORKS
    3.
    发明公开
    ASYNCHRONOUS OPTIMIZATION FOR SEQUENCE TRAINING OF NEURAL NETWORKS 审中-公开

    公开(公告)号：US20240087559A1

    公开(公告)日：2024-03-14

    申请号：US18506540

    申请日：2023-11-10

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Erik Mcdermott ,  Vincent O. Vanhoucke ,  Andrew W. Senior ,  Michiel A. U. Bacchiani

    IPC: G10L15/06 ,  G06N3/045 ,  G10L15/16 ,  G10L15/183

    CPC classification number: G10L15/063 ,  G06N3/045 ,  G10L15/16 ,  G10L15/183

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.

    Asynchronous optimization for sequence training of neural networks
    4.
    发明授权
    Asynchronous optimization for sequence training of neural networks 有权

    公开(公告)号：US10916238B2

    公开(公告)日：2021-02-09

    申请号：US16863432

    申请日：2020-04-30

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Erik Mcdermott ,  Vincent O. Vanhoucke ,  Andrew W. Senior ,  Michiel A. U. Bacchiani

    IPC: G10L15/06 ,  G10L15/16 ,  G10L15/183 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.

    Speech recognition with acoustic models
    5.
    发明授权
    Speech recognition with acoustic models 有权

    公开(公告)号：US10192556B2

    公开(公告)日：2019-01-29

    申请号：US15810516

    申请日：2017-11-13

    Applicant: Google LLC

    Inventor： Hasim Sak ,  Andrew W. Senior

    IPC: G10L17/14 ,  G10L15/02 ,  G10L15/16 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for learning pronunciations from acoustic sequences. One method includes receiving an acoustic sequence, the acoustic sequence representing an utterance, and the acoustic sequence comprising a sequence of multiple frames of acoustic data at each of a plurality of time steps; stacking one or more frames of acoustic data to generate a sequence of modified frames of acoustic data; processing the sequence of modified frames of acoustic data through an acoustic modeling neural network comprising one or more recurrent neural network (RNN) layers and a final CTC output layer to generate a neural network output, wherein processing the sequence of modified frames of acoustic data comprises: subsampling the modified frames of acoustic data; and processing each subsampled modified frame of acoustic data through the acoustic modeling neural network.

    Frequency warping in a speech recognition system
    8.
    发明授权
    Frequency warping in a speech recognition system 有权

    公开(公告)号：US10026396B2

    公开(公告)日：2018-07-17

    申请号：US15221491

    申请日：2016-07-27

    Applicant: Google LLC

    Inventor： Andrew W. Senior

    IPC: G10L15/16 ,  G10L15/02 ,  G10L15/06 ,  G10L25/30 ,  G10L21/013

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving a sequence representing an utterance, the sequence comprising a plurality of audio frames; determining one or more warping factors for each audio frame in the sequence using a warping neural network; applying, for each audio frame, the one or more warping factors for the audio frame to the audio frame to generate a respective modified audio frame, wherein the applying comprises using at least one of the warping factors to scale a respective frequency of the audio frame to a new respective frequency in the respective modified audio frame; and decoding the modified audio frames using a decoding neural network, wherein the decoding neural network is configured to output a word sequence that is a transcription of the utterance.

    Asynchronous optimization for sequence training of neural networks
    10.
    发明授权
    Asynchronous optimization for sequence training of neural networks 有权

    公开(公告)号：US11854534B1

    公开(公告)日：2023-12-26

    申请号：US18069035

    申请日：2022-12-20

    Applicant: Google LLC

    Inventor： Georg Heigold ,  Erik Mcdermott ,  Vincent O. Vanhoucke ,  Andrew W. Senior ,  Michiel A. U. Bacchiani

    IPC: G10L15/06 ,  G10L15/183 ,  G06N3/045

    CPC classification number: G10L15/063 ,  G06N3/045 ,  G10L15/06 ,  G10L15/183

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for obtaining, by a first sequence-training speech model, a first batch of training frames that represent speech features of first training utterances; obtaining, by the first sequence-training speech model, one or more first neural network parameters; determining, by the first sequence-training speech model, one or more optimized first neural network parameters based on (i) the first batch of training frames and (ii) the one or more first neural network parameters; obtaining, by a second sequence-training speech model, a second batch of training frames that represent speech features of second training utterances; obtaining one or more second neural network parameters; and determining, by the second sequence-training speech model, one or more optimized second neural network parameters based on (i) the second batch of training frames and (ii) the one or more second neural network parameters.

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