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    Generating neural network outputs using insertion commands
    41.
    发明授权
    Generating neural network outputs using insertion commands 有权

    公开(公告)号：US11657277B2

    公开(公告)日：2023-05-23

    申请号：US16883772

    申请日：2020-05-26

    Applicant: Google LLC

    Inventor： William Chan ,  Mitchell Thomas Stern ,  Nikita Kitaev ,  Kelvin Gu ,  Jakob D. Uszkoreit

    IPC: G10L25/00 ,  G10L21/00 ,  G06F40/30 ,  G06N3/08 ,  G06F40/237 ,  G06N3/04 ,  G06N3/084

    CPC classification number: G06N3/08 ,  G06F40/237 ,  G06N3/04 ,  G06N3/084

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing sequence modeling tasks using insertions. One of the methods includes receiving a system input that includes one or more source elements from a source sequence and zero or more target elements from a target sequence, wherein each source element is selected from a vocabulary of source elements and wherein each target element is selected from a vocabulary of target elements; generating a partial concatenated sequence that includes the one or more source elements from the source sequence and the zero or more target elements from the target sequence, wherein the source and target elements arranged in the partial concatenated sequence according to a combined order; and generating a final concatenated sequence that includes a finalized source sequence and a finalized target sequence, wherein the finalized target sequence includes one or more target elements.

    Speech recognition with attention-based recurrent neural networks
    46.
    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.

    Training sequence generation neural networks using quality scores
    47.
    发明授权
    Training sequence generation neural networks using quality scores 有权

    公开(公告)号：US10540585B2

    公开(公告)日：2020-01-21

    申请号：US16421406

    申请日：2019-05-23

    Applicant: Google LLC

    Inventor： Mohammad Norouzi ,  William Chan ,  Sara Sabour Rouh Aghdam

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

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training a sequence generation neural network. One of the methods includes obtaining a batch of training examples; for each of the training examples: processing the training network input in the training example using the neural network to generate an output sequence; for each particular output position in the output sequence: identifying a prefix that includes the system outputs at positions before the particular output position in the output sequence, for each possible system output in the vocabulary, determining a highest quality score that can be assigned to any candidate output sequence that includes the prefix followed by the possible system output, and determining an update to the current values of the network parameters that increases a likelihood that the neural network generates a system output at the position that has a high quality score.

    PROCESSING TEXT USING NEURAL NETWORKS
    48.
    发明申请
    PROCESSING TEXT USING NEURAL NETWORKS 审中-公开

    公开(公告)号：US20190258713A1

    公开(公告)日：2019-08-22

    申请号：US16283632

    申请日：2019-02-22

    Applicant: Google LLC

    Inventor： Jamie Ryan Kiros ,  William Chan ,  Geoffrey E. Hinton

    IPC: G06F17/27 ,  G06F16/53 ,  G06N3/08 ,  G06N3/04 ,  G06K9/72

    Abstract: Methods, systems, and apparatus including computer programs encoded on a computer storage medium, for generating a data set that associates each text segment in a vocabulary of text segments with a respective numeric embedding. In one aspect, a method includes providing, to an image search engine, a search query that includes the text segment; obtaining image search results that have been classified as being responsive to the search query by the image search engine, wherein each image search result identifies a respective image; for each image search result, processing the image identified by the image search result using a convolutional neural network, wherein the convolutional neural network has been trained to process the image to generate an image numeric embedding for the image; and generating a numeric embedding for the text segment from the image numeric embeddings for the images identified by the image search results.

    VERY DEEP CONVOLUTIONAL NEURAL NETWORKS FOR END-TO-END SPEECH RECOGNITION
    49.
    发明申请
    VERY DEEP CONVOLUTIONAL NEURAL NETWORKS FOR END-TO-END SPEECH RECOGNITION 审中-公开

    公开(公告)号：US20190236451A1

    公开(公告)日：2019-08-01

    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.

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