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    PROCESSING TEXT SEQUENCES USING NEURAL NETWORKS
    22.
    发明申请
    PROCESSING TEXT SEQUENCES USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20200026765A1

    公开(公告)日：2020-01-23

    申请号：US16338174

    申请日：2017-10-03

    Applicant: Google LLC

    Inventor： Navdeep Jaitly ,  Yu Zhang ,  Quoc V. Le ,  William Chan

    IPC: G06F17/28 ,  G10L15/16 ,  G10L15/197 ,  G06N3/08

    Abstract: A computer-implemented method for training a neural network that is configured to generate a score distribution over a set of multiple output positions. The neural network is configured to process a network input to generate a respective score distribution for each of a plurality of output positions including a respective score for each token in a predetermined set of tokens that includes n-grams of multiple different sizes. Example methods described herein provide trained neural networks which produce results with improved accuracy compared to the state of the art, e.g. translations that are more accurate compared to the state of the art, or more accurate speech recognition compared to the state of the art.

    Very deep convolutional neural networks for end-to-end speech recognition
    23.
    发明授权
    Very deep convolutional neural networks for end-to-end speech recognition 有权

    公开(公告)号：US10510004B2

    公开(公告)日：2019-12-17

    申请号：US16380101

    申请日：2019-04-10

    Applicant: Google LLC

    Inventor： Navdeep Jaitly ,  Yu Zhang ,  William Chan

    IPC: G06N3/08 ,  G06N3/04 ,  G10L15/16 ,  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 substring scores that includes a respective substring score for each substring in a set of substrings.

    Recurrent neural networks for online sequence generation
    27.
    发明授权
    Recurrent neural networks for online sequence generation 有权

    公开(公告)号：US11625572B2

    公开(公告)日：2023-04-11

    申请号：US16610466

    申请日：2018-05-03

    Applicant: GOOGLE LLC

    Inventor： Chung-Cheng Chiu ,  Navdeep Jaitly ,  John Dieterich Lawson ,  George Jay Tucker

    IPC: G06N3/04 ,  G06N3/08

    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 an 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
    30.
    发明授权
    Speech recognition with attention-based recurrent neural networks 有权

    公开(公告)号：US10540962B1

    公开(公告)日：2020-01-21

    申请号：US15970662

    申请日：2018-05-03

    Applicant: Google LLC

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

    IPC: G10L15/16 ,  G10L15/26 ,  G06F17/22 ,  G10L15/183 ,  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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