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    Generating Diverse and Natural Text-To-Speech Samples
    93.
    发明申请
    Generating Diverse and Natural Text-To-Speech Samples 有权

    公开(公告)号：US20220246132A1

    公开(公告)日：2022-08-04

    申请号：US17163007

    申请日：2021-01-29

    Applicant: Google LLC

    Inventor： Yu Zhang ,  Bhuvana Ramabhadran ,  Andrew Rosenberg ,  Yonghui Wu ,  Byungha Chun ,  Ron Weiss ,  Yuan Cao

    IPC: G10L13/047 ,  G10L25/18 ,  G10L13/10 ,  G10L15/06 ,  G06N3/08

    Abstract: A method of generating diverse and natural text-to-speech (TTS) samples includes receiving a text and generating a speech sample based on the text using a TTS model. A training process trains the TTS model to generate the speech sample by receiving training samples. Each training sample includes a spectrogram and a training text corresponding to the spectrogram. For each training sample, the training process identifies speech units associated with the training text. For each speech unit, the training process generates a speech embedding, aligns the speech embedding with a portion of the spectrogram, extracts a latent feature from the aligned portion of the spectrogram, and assigns a quantized embedding to the latent feature. The training process generates the speech sample by decoding a concatenation of the speech embeddings and a quantized embeddings for the speech units associated with the training text corresponding to the spectrogram.

    Neural machine translation systems
    95.
    发明授权
    Neural machine translation systems 有权

    公开(公告)号：US11113480B2

    公开(公告)日：2021-09-07

    申请号：US16336870

    申请日：2017-09-25

    Applicant: GOOGLE LLC

    Inventor： Mohammad Norouzi ,  Zhifeng Chen ,  Yonghui Wu ,  Michael Schuster ,  Quoc V. Le

    IPC: G06F40/58 ,  G06F40/44 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural machine translation. One of the systems includes an encoder neural network comprising: an input forward long short-term memory (LSTM) layer configured to process each input token in the input sequence in a forward order to generate a respective forward representation of each input token, an input backward LSTM layer configured to process each input token in a backward order to generate a respective backward representation of each input token and a plurality of hidden LSTM layers configured to process a respective combined representation of each of the input tokens in the forward order to generate a respective encoded representation of each of the input tokens; and a decoder subsystem configured to receive the respective encoded representations and to process the encoded representations to generate an output sequence.

    MINIMUM WORD ERROR RATE TRAINING FOR ATTENTION-BASED SEQUENCE-TO-SEQUENCE MODELS
    97.
    发明申请
    MINIMUM WORD ERROR RATE TRAINING FOR ATTENTION-BASED SEQUENCE-TO-SEQUENCE MODELS 审中-公开

    公开(公告)号：US20200043483A1

    公开(公告)日：2020-02-06

    申请号：US16529252

    申请日：2019-08-01

    Applicant: Google LLC

    Inventor： Rohit Prakash Prabhavalkar ,  Tara N. Sainath ,  Yonghui Wu ,  Patrick An Phu Nguyen ,  Zhifeng Chen ,  Chung-Cheng Chiu ,  Anjuli Patricia Kannan

    IPC: G10L15/197 ,  G10L15/16 ,  G10L15/22 ,  G10L15/06 ,  G10L15/02

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-readable storage media, for speech recognition using attention-based sequence-to-sequence models. In some implementations, audio data indicating acoustic characteristics of an utterance is received. A sequence of feature vectors indicative of the acoustic characteristics of the utterance is generated. The sequence of feature vectors is processed using a speech recognition model that has been trained using a loss function that uses N-best lists of decoded hypotheses, the speech recognition model including an encoder, an attention module, and a decoder. The encoder and decoder each include one or more recurrent neural network layers. A sequence of output vectors representing distributions over a predetermined set of linguistic units is obtained. A transcription for the utterance is obtained based on the sequence of output vectors. Data indicating the transcription of the utterance is provided.

    NEURAL MACHINE TRANSLATION SYSTEMS
    98.
    发明申请
    NEURAL MACHINE TRANSLATION SYSTEMS 审中-公开

    公开(公告)号：US20200034435A1

    公开(公告)日：2020-01-30

    申请号：US16336870

    申请日：2017-09-25

    Applicant: GOOGLE LLC

    Inventor： Mohammad Norouzi ,  Zhifeng Chen ,  Yonghui Wu ,  Michael Schuster ,  Quoc V. Le

    IPC: G06F17/28 ,  G06N3/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for neural machine translation. One of the systems includes an encoder neural network comprising: an input forward long short-term memory (LSTM) layer configured to process each input token in the input sequence in a forward order to generate a respective forward representation of each input token, an input backward LSTM layer configured to process each input token in a backward order to generate a respective backward representation of each input token and a plurality of hidden LSTM layers configured to process a respective combined representation of each of the input tokens in the forward order to generate a respective encoded representation of each of the input tokens; and a decoder subsystem configured to receive the respective encoded representations and to process the encoded representations to generate an output sequence.

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