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    SEQUENCE MODELING USING IMPUTATION
    1.
    发明申请
    SEQUENCE MODELING USING IMPUTATION 有权

    公开(公告)号：US20230075716A1

    公开(公告)日：2023-03-09

    申请号：US17797872

    申请日：2021-02-08

    Applicant: Google LLC

    Inventor： William Chan ,  Chitwan Saharia ,  Geoffrey E. Hinton ,  Mohammad Norouzi ,  Navdeep Jaitly

    IPC: G06F40/47 ,  G06F40/284

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sequence modeling. One of the methods includes receiving an input sequence having a plurality of input positions; determining a plurality of blocks of consecutive input positions; processing the input sequence using a neural network to generate a latent alignment, comprising, at each of a plurality of input time steps: receiving a partial latent alignment from a previous input time step; selecting an input position in each block, wherein the token at the selected input position of the partial latent alignment in each block is a mask token; and processing the partial latent alignment and the input sequence using the neural network to generate a new latent alignment, wherein the new latent alignment comprises, at the selected input position in each block, an output token or a blank token; and generating, using the latent alignment, an output sequence.

    SPEECH RECOGNITION WITH ATTENTION-BASED RECURRENT NEURAL NETWORKS
    2.
    发明申请
    SPEECH RECOGNITION WITH ATTENTION-BASED RECURRENT NEURAL NETWORKS 有权

    公开(公告)号：US20220028375A1

    公开(公告)日：2022-01-27

    申请号：US17450235

    申请日：2021-10-07

    Applicant: Google LLC

    Inventor： William Chan ,  Navdeep Jaitly ,  Quoc V. Le ,  Oriol Vinyals ,  Noam M. Shazeer

    IPC: G10L15/16 ,  G06N3/04 ,  G06F40/12 ,  G06F40/197 ,  G10L15/183 ,  G10L15/26

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for speech recognition. One method includes obtaining an input acoustic sequence, the input acoustic sequence representing an utterance, and the input acoustic sequence comprising a respective acoustic feature representation at each of a first number of time steps; processing the input acoustic sequence using a first neural network to convert the input acoustic sequence into an alternative representation for the input acoustic sequence; processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings; and generating a sequence of substrings that represent a transcription of the utterance.

    Very deep convolutional neural networks for end-to-end speech recognition
    4.
    发明授权
    Very deep convolutional neural networks for end-to-end speech recognition 有权

    公开(公告)号：US11080599B2

    公开(公告)日：2021-08-03

    申请号：US16692538

    申请日：2019-11-22

    Applicant: Google LLC

    Inventor： Navdeep Jaitly ,  Yu Zhang ,  William Chan

    IPC: G06N3/08 ,  G10L15/16 ,  G06N3/04 ,  G10L15/02 ,  G10L15/22

    Abstract: A speech recognition neural network system includes an encoder neural network and a decoder neural network. The encoder neural network generates an encoded sequence from an input acoustic sequence that represents an utterance. The input acoustic sequence includes a respective acoustic feature representation at each of a plurality of input time steps, the encoded sequence includes a respective encoded representation at each of a plurality of time reduced time steps, and the number of time reduced time steps is less than the number of input time steps. The encoder neural network includes a time reduction subnetwork, a convolutional LSTM subnetwork, and a network in network subnetwork. The decoder neural network receives the encoded sequence and processes the encoded sequence to generate, for each position in an output sequence order, a set of sub string scores that includes a respective sub string score for each substring in a set of substrings.

    Recurrent neural networks for online sequence generation
    5.
    发明授权
    Recurrent neural networks for online sequence generation 有权

    公开(公告)号：US10656605B1

    公开(公告)日：2020-05-19

    申请号：US16401791

    申请日：2019-05-02

    Applicant: Google LLC

    Inventor： Chung-Cheng Chiu ,  Navdeep Jaitly ,  Ilya Sutskever ,  Yuping Luo

    IPC: G05B13/02 ,  G10L15/16 ,  G06N3/04 ,  G06F40/44

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating a target sequence from a source sequence. In one aspect, the system includes a recurrent neural network configured to, at each time step, receive am input for the time step and process the input to generate a progress score and a set of output scores; and a subsystem configured to, at each time step, generate the recurrent neural network input and provide the input to the recurrent neural network; determine, from the progress score, whether or not to emit a new output at the time step; and, in response to determining to emit a new output, select an output using the output scores and emit the selected output as the output at a next position in the output order.

    Speech recognition with attention-based recurrent neural networks
    8.
    发明授权
    Speech recognition with attention-based recurrent neural networks 有权

    公开(公告)号：US11151985B2

    公开(公告)日：2021-10-19

    申请号：US16713298

    申请日：2019-12-13

    Applicant: Google LLC

    Inventor： William Chan ,  Navdeep Jaitly ,  Quoc V. Le ,  Oriol Vinyals ,  Noam M. Shazeer

    IPC: G10L15/16 ,  G06N3/04 ,  G06F40/12 ,  G06F40/197 ,  G10L15/183 ,  G10L15/26 ,  G10L25/30

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media for speech recognition. One method includes obtaining an input acoustic sequence, the input acoustic sequence representing an utterance, and the input acoustic sequence comprising a respective acoustic feature representation at each of a first number of time steps, processing the input acoustic sequence using a first neural network to convert the input acoustic sequence into an alternative representation for the input acoustic sequence, processing the alternative representation for the input acoustic sequence using an attention-based Recurrent Neural Network (RNN) to generate, for each position in an output sequence order, a set of substring scores that includes a respective substring score for each substring in a set of substrings; and generating a sequence of substrings that represent a transcription of the utterance.

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