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    Phonemes and graphemes for neural text-to-speech
    41.
    发明授权
    Phonemes and graphemes for neural text-to-speech 有权

    公开(公告)号：US12020685B2

    公开(公告)日：2024-06-25

    申请号：US17643684

    申请日：2021-12-10

    Applicant: Google LLC

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

    IPC: G10L13/08 ,  G06F40/263 ,  G06F40/279 ,  G06N3/08 ,  G10L13/047

    CPC classification number: G10L13/086 ,  G06F40/263 ,  G06F40/279 ,  G06N3/08 ,  G10L13/047

    Abstract: A method includes receiving a text input including a sequence of words represented as an input encoder embedding. The input encoder embedding includes a plurality of tokens, with the plurality of tokens including a first set of grapheme tokens representing the text input as respective graphemes and a second set of phoneme tokens representing the text input as respective phonemes. The method also includes, for each respective phoneme token of the second set of phoneme tokens: identifying a respective word of the sequence of words corresponding to the respective phoneme token and determining a respective grapheme token representing the respective word of the sequence of words corresponding to the respective phoneme token. The method also includes generating an output encoder embedding based on a relationship between each respective phoneme token and the corresponding grapheme token determined to represent a same respective word as the respective phoneme token.

    Vector-Quantized Image Modeling
    42.
    发明公开
    Vector-Quantized Image Modeling 审中-公开

    公开(公告)号：US20240112088A1

    公开(公告)日：2024-04-04

    申请号：US18520083

    申请日：2023-11-27

    Applicant: Google LLC

    Inventor： Jiahui Yu ,  Xin Li ,  Han Zhang ,  Vijay Vasudevan ,  Alexander Yeong-Shiuh Ku ,  Jason Michael Baldridge ,  Yuanzhong Xu ,  Jing Yu Koh ,  Thang Minh Luong ,  Gunjan Baid ,  Zirui Wang ,  Yonghui Wu

    IPC: G06N20/00

    CPC classification number: G06N20/00

    Abstract: Systems and methods are provided for vector-quantized image modeling using vision transformers and improved codebook handling. In particular, the present disclosure provides a Vector-quantized Image Modeling (VIM) approach that involves pretraining a machine learning model (e.g., Transformer model) to predict rasterized image tokens autoregressively. The discrete image tokens can be encoded from a learned Vision-Transformer-based VQGAN (example implementations of which can be referred to as ViT-VQGAN). The present disclosure proposes multiple improvements over vanilla VQGAN from architecture to codebook learning, yielding better efficiency and reconstruction fidelity. The improved ViT-VQGAN further improves vector-quantized image modeling tasks, including unconditional image generation, conditioned image generation (e.g., class-conditioned image generation), and unsupervised representation learning.

    Building a text-to-speech system from a small amount of speech data
    47.
    发明授权
    Building a text-to-speech system from a small amount of speech data 有权

    公开(公告)号：US11335321B2

    公开(公告)日：2022-05-17

    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: G10L13/08 ,  G10L13/04 ,  G10L13/033 ,  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.

    Fast Emit Low-latency Streaming ASR with Sequence-level Emission Regularization
    48.
    发明申请
    Fast Emit Low-latency Streaming ASR with Sequence-level Emission Regularization 有权

    公开(公告)号：US20220122586A1

    公开(公告)日：2022-04-21

    申请号：US17447285

    申请日：2021-09-09

    Applicant: Google LLC

    Inventor： Jiahui Yu ,  Chung-cheng Chiu ,  Bo Li ,  Shuo-yiin Chang ,  Tara Sainath ,  Wei Han ,  Anmol Gulati ,  Yanzhang He ,  Arun Narayanan ,  Yonghui Wu ,  Ruoming Pang

    IPC: G10L15/06 ,  G10L15/22 ,  G10L15/30 ,  G10L15/16

    Abstract: A computer-implemented method of training a streaming speech recognition model that includes receiving, as input to the streaming speech recognition model, a sequence of acoustic frames. The streaming speech recognition model is configured to learn an alignment probability between the sequence of acoustic frames and an output sequence of vocabulary tokens. The vocabulary tokens include a plurality of label tokens and a blank token. At each output step, the method includes determining a first probability of emitting one of the label tokens and determining a second probability of emitting the blank token. The method also includes generating the alignment probability at a sequence level based on the first probability and the second probability. The method also includes applying a tuning parameter to the alignment probability at the sequence level to maximize the first probability of emitting one of the label tokens.

    Parallel Tacotron Non-Autoregressive and Controllable TTS
    49.
    发明申请
    Parallel Tacotron Non-Autoregressive and Controllable TTS 有权

    公开(公告)号：US20220122582A1

    公开(公告)日：2022-04-21

    申请号：US17327076

    申请日：2021-05-21

    Applicant: Google LLC

    Inventor： Isaac Elias ,  Jonathan Shen ,  Yu Zhang ,  Ye Jia ,  Ron J. Weiss ,  Yonghui Wu ,  Byungha Chun

    IPC: G10L13/08 ,  G10L21/10 ,  G06F40/126 ,  G06N3/08

    Abstract: A method for training a non-autoregressive TTS model includes receiving training data that includes a reference audio signal and a corresponding input text sequence. The method also includes encoding the reference audio signal into a variational embedding that disentangles the style/prosody information from the reference audio signal and encoding the input text sequence into an encoded text sequence. The method also includes predicting a phoneme duration for each phoneme in the input text sequence and determining a phoneme duration loss based on the predicted phoneme durations and a reference phoneme duration. The method also includes generating one or more predicted mel-frequency spectrogram sequences for the input text sequence and determining a final spectrogram loss based on the predicted mel-frequency spectrogram sequences and a reference mel-frequency spectrogram sequence. The method also includes training the TTS model based on the final spectrogram loss and the corresponding phoneme duration loss.

    Multi-dialect and multilingual speech recognition
    50.
    发明授权
    Multi-dialect and multilingual speech recognition 有权

    公开(公告)号：US11238845B2

    公开(公告)日：2022-02-01

    申请号：US16684483

    申请日：2019-11-14

    Applicant: GOOGLE LLC

    Inventor： Zhifeng Chen ,  Bo Li ,  Eugene Weinstein ,  Yonghui Wu ,  Pedro J. Moreno Mengibar ,  Ron J. Weiss ,  Khe Chai Sim ,  Tara N. Sainath ,  Patrick An Phu Nguyen

    IPC: G10L15/00 ,  G10L15/07 ,  G10L15/16 ,  G10L15/06

    Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer-readable media, for speech recognition using multi-dialect and multilingual models. In some implementations, audio data indicating audio characteristics of an utterance is received. Input features determined based on the audio data are provided to a speech recognition model that has been trained to output score indicating the likelihood of linguistic units for each of multiple different language or dialects. The speech recognition model can be one that has been trained using cluster adaptive training. Output that the speech recognition model generated in response to receiving the input features determined based on the audio data is received. A transcription of the utterance generated based on the output of the speech recognition model is provided.

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