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    RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION
    1.
    发明公开
    RESIDUAL ADAPTERS FOR FEW-SHOT TEXT-TO-SPEECH SPEAKER ADAPTATION 审中-公开

    公开(公告)号：US20240135915A1

    公开(公告)日：2024-04-25

    申请号：US18493770

    申请日：2023-10-23

    Applicant: Google LLC

    Inventor： Nobuyuki Morioka ,  Byungha Chun ,  Nanxin Chen ,  Yu Zhang ,  Yifan Ding

    IPC: G10L13/027

    CPC classification number: G10L13/027

    Abstract: A method for residual adapters for few-shot text-to-speech speaker adaptation includes obtaining a text-to-speech (TTS) model configured to convert text into representations of synthetic speech, the TTS model pre-trained on an initial training data set. The method further includes augmenting the TTS model with a stack of residual adapters. The method includes receiving an adaption training data set including one or more spoken utterances spoken by a target speaker, each spoken utterance in the adaptation training data set paired with corresponding input text associated with a transcription of the spoken utterance. The method also includes adapting, using the adaption training data set, the TTS model augmented with the stack of residual adapters to learn how to synthesize speech in a voice of the target speaker by optimizing the stack of residual adapters while parameters of the TTS model are frozen.

    END-TO-END SPEECH WAVEFORM GENERATION THROUGH DATA DENSITY GRADIENT ESTIMATION
    2.
    发明公开
    END-TO-END SPEECH WAVEFORM GENERATION THROUGH DATA DENSITY GRADIENT ESTIMATION 审中-公开

    公开(公告)号：US20230252974A1

    公开(公告)日：2023-08-10

    申请号：US18010438

    申请日：2021-09-02

    Applicant: Google LLC

    Inventor： Byungha Chun ,  Mohammad Norouzi ,  Nanxin Chen ,  Ron J. Weiss ,  William Chan ,  Yu Zhang ,  Yonghui Wu

    IPC: G10L13/08 ,  G10L21/0208

    CPC classification number: G10L13/08 ,  G10L21/0208

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating waveforms conditioned on phoneme sequences. In one aspect, a method comprises: obtaining a phoneme sequence; processing the phoneme sequence using an encoder neural network to generate a hidden representation of the phoneme sequence; generating, from the hidden representation, a conditioning input; initializing a current waveform output; and generating a final waveform output that defines an utterance of the phoneme sequence by a speaker by updating the current waveform output at each of a plurality of iterations, wherein each iteration corresponds to a respective noise level, and wherein the updating comprises, at each iteration: processing (i) the current waveform output and (ii) the conditioning input using a noise estimation neural network to generate a noise output; and updating the current waveform output using the noise output and the noise level for the iteration.

    Two-Level Text-To-Speech Systems Using Synthetic Training Data
    3.
    发明申请
    Two-Level Text-To-Speech Systems Using Synthetic Training Data 有权

    公开(公告)号：US20230018384A1

    公开(公告)日：2023-01-19

    申请号：US17305809

    申请日：2021-07-14

    Applicant: Google LLC

    Inventor： Lev Finkelstein ,  Chun-an Chan ,  Byungha Chun ,  Norman Casagrande ,  Yu Zhang ,  Robert Andrew James Clark ,  Vincent Wan

    IPC: G10L13/08 ,  G10L13/047

    Abstract: A method includes obtaining training data including a plurality of training audio signals and corresponding transcripts. Each training audio signal is spoken by a target speaker in a first accent/dialect. For each training audio signal of the training data, the method includes generating a training synthesized speech representation spoken by the target speaker in a second accent/dialect different than the first accent/dialect and training a text-to-speech (TTS) system based on the corresponding transcript and the training synthesized speech representation. The method also includes receiving an input text utterance to be synthesized into speech in the second accent/dialect. The method also includes obtaining conditioning inputs that include a speaker embedding and an accent/dialect identifier that identifies the second accent/dialect. The method also includes generating an output audio waveform corresponding to a synthesized speech representation of the input text sequence that clones the voice of the target speaker in the second accent/dialect.

    Building a Text-to-Speech System from a Small Amount of Speech Data
    4.
    发明申请
    Building a Text-to-Speech System from a Small Amount of Speech Data 有权

    公开(公告)号：US20220068256A1

    公开(公告)日：2022-03-03

    申请号：US17005974

    申请日：2020-08-28

    Applicant: Google LLC

    Inventor： Ye Jia ,  Byungha Chun ,  Yusuke Oda ,  Norman Casagrande ,  Tejas Iyer ,  Fan Luo ,  Russell John Wyatt Skerry-Ryan ,  Jonathan Shen ,  Yonghui Wu ,  Yu Zhang

    IPC: G10L13/04 ,  G10L13/033 ,  G10L13/08 ,  G10L15/06

    Abstract: A method of building a text-to-speech (TTS) system from a small amount of speech data includes receiving a first plurality of recorded speech samples from an assortment of speakers and a second plurality of recorded speech samples from a target speaker where the assortment of speakers does not include the target speaker. The method further includes training a TTS model using the first plurality of recorded speech samples from the assortment of speakers. Here, the trained TTS model is configured to output synthetic speech as an audible representation of a text input. The method also includes re-training the trained TTS model using the second plurality of recorded speech samples from the target speaker combined with the first plurality of recorded speech samples from the assortment of speakers. Here, the re-trained TTS model is configured to output synthetic speech resembling speaking characteristics of the target speaker.

    Two-Level Text-To-Speech Systems Using Synthetic Training Data
    5.
    发明申请
    Two-Level Text-To-Speech Systems Using Synthetic Training Data 有权

    公开(公告)号：US20250078808A1

    公开(公告)日：2025-03-06

    申请号：US18949095

    申请日：2024-11-15

    Applicant: Google LLC

    Inventor： Lev Finkelstein ,  Chun-an Chan ,  Byungha Chun ,  Norman Casagrande ,  Yu Zhang ,  Robert Andrew James Clark ,  Vincent Wan

    IPC: G10L13/08 ,  G10L13/047

    Abstract: A method includes obtaining training data including a plurality of training audio signals and corresponding transcripts. Each training audio signal is spoken by a target speaker in a first accent/dialect. For each training audio signal of the training data, the method includes generating a training synthesized speech representation spoken by the target speaker in a second accent/dialect different than the first accent/dialect and training a text-to-speech (TTS) system based on the corresponding transcript and the training synthesized speech representation. The method also includes receiving an input text utterance to be synthesized into speech in the second accent/dialect. The method also includes obtaining conditioning inputs that include a speaker embedding and an accent/dialect identifier that identifies the second accent/dialect. The method also includes generating an output audio waveform corresponding to a synthesized speech representation of the input text sequence that clones the voice of the target speaker in the second accent/dialect.

    Generating diverse and natural text-to-speech samples
    8.
    发明授权
    Generating diverse and natural text-to-speech samples 有权

    公开(公告)号：US11475874B2

    公开(公告)日：2022-10-18

    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: G10L25/30 ,  G10L25/00 ,  G10L17/00 ,  G10L13/047 ,  G10L25/18 ,  G06N3/08 ,  G10L15/06 ,  G10L13/10

    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.

    Unsupervised Parallel Tacotron Non-Autoregressive and Controllable Text-To-Speech
    9.
    发明申请
    Unsupervised Parallel Tacotron Non-Autoregressive and Controllable Text-To-Speech 有权

    公开(公告)号：US20220301543A1

    公开(公告)日：2022-09-22

    申请号：US17326542

    申请日：2021-05-21

    Applicant: Google LLC

    Inventor： Isaac Elias ,  Byungha Chun ,  Jonathan Shen ,  Ye Jia ,  Yu Zhang ,  Yonghui Wu

    IPC: G10L13/08 ,  G10L13/04

    Abstract: A method for training a non-autoregressive TTS model includes obtaining a sequence representation of an encoded text sequence concatenated with a variational embedding. The method also includes using a duration model network to predict a phoneme duration for each phoneme represented by the encoded text sequence. Based on the predicted phoneme durations, the method also includes learning an interval representation and an auxiliary attention context representation. The method also includes upsampling, using the interval representation and the auxiliary attention context representation, the sequence representation into an upsampled output specifying a number of frames. The method also includes generating, based on the upsampled output, one or more predicted mel-frequency spectrogram sequences for the encoded text sequence. The method also includes determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence and training the TTS model based on the final spectrogram loss.

    Text-to-speech using duration prediction
    10.
    发明授权
    Text-to-speech using duration prediction 有权

    公开(公告)号：US12100382B2

    公开(公告)日：2024-09-24

    申请号：US17492543

    申请日：2021-10-01

    Applicant: Google LLC

    Inventor： Yu Zhang ,  Isaac Elias ,  Byungha Chun ,  Ye Jia ,  Yonghui Wu ,  Mike Chrzanowski ,  Jonathan Shen

    IPC: G10L13/027 ,  G10L13/04

    CPC classification number: G10L13/027 ,  G10L13/04

    Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, synthesizing audio data from text data using duration prediction. One of the methods includes processing an input text sequence that includes a respective text element at each of multiple input time steps using a first neural network to generate a modified input sequence comprising, for each input time step, a representation of the corresponding text element in the input text sequence; processing the modified input sequence using a second neural network to generate, for each input time step, a predicted duration of the corresponding text element in the output audio sequence; upsampling the modified input sequence according to the predicted durations to generate an intermediate sequence comprising a respective intermediate element at each of a plurality of intermediate time steps; and generating an output audio sequence using the intermediate sequence.

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