公开(公告)号：US11514888B2

公开(公告)日：2022-11-29

申请号：US16992410

申请日：2020-08-13

Applicant: Google LLC

Inventor： Lev Finkelstein ,  Chun-An Chan ,  Byungha Chun ,  Ye Jia ,  Yu Zhang ,  Robert Andrew James Clark ,  Vincent Wan

IPC: G10L13/10 ,  G10L13/02 ,  G10L17/18

Abstract: A method includes receiving an input text utterance to be synthesized into expressive speech having an intended prosody and a target voice and generating, using a first text-to-speech (TTS) model, an intermediate synthesized speech representation tor the input text utterance. The intermediate synthesized speech representation possesses the intended prosody. The method also includes providing the intermediate synthesized speech representation to a second TTS model that includes an encoder portion and a decoder portion. The encoder portion is configured to encode the intermediate synthesized speech representation into an utterance embedding that specifies the intended prosody. The decoder portion is configured to process the input text utterance and the utterance embedding to generate an output audio signal of expressive speech that has the intended prosody specified by the utterance embedding and speaker characteristics of the target voice.

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.

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.