公开(公告)号：US20240112667A1

公开(公告)日：2024-04-04

申请号：US18525475

申请日：2023-11-30

Applicant: Google LLC

Inventor： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Ignacio Lopez Moreno ,  Fei Ren ,  Yu Zhang ,  Quan Wang ,  Patrick An Phu Nguyen

IPC: G10L13/04 ,  G10L17/04 ,  G10L19/00

CPC classification number: G10L13/04 ,  G10L17/04 ,  G10L19/00 ,  G06N3/08

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

公开(公告)号：US11908448B2

公开(公告)日：2024-02-20

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 ,  G10L13/047 ,  G06F40/126 ,  G10L21/10 ,  G06N3/08 ,  G06N3/088 ,  G06N3/044 ,  G06N3/045 ,  G06N3/048

CPC classification number: G10L13/08 ,  G06F40/126 ,  G06N3/044 ,  G06N3/045 ,  G06N3/08 ,  G06N3/088 ,  G10L13/047 ,  G10L21/10 ,  G06N3/048

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.

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.

公开(公告)号：US11848002B2

公开(公告)日：2023-12-19

申请号：US17813361

申请日：2022-07-19

Applicant: Google LLC

Inventor： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Ignacio Lopez Moreno ,  Fei Ren ,  Yu Zhang ,  Quan Wang ,  Patrick An Phu Nguyen

IPC: G10L13/04 ,  G10L17/04 ,  G10L19/00 ,  G06N3/08 ,  G10L13/02

CPC classification number: G10L13/04 ,  G10L17/04 ,  G10L19/00 ,  G06N3/08 ,  G10L2013/021

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

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.

公开(公告)号：US10971170B2

公开(公告)日：2021-04-06

申请号：US16058640

申请日：2018-08-08

Applicant: Google LLC

Inventor： Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Michael Schuster ,  Navdeep Jaitly ,  Zongheng Yang ,  Zhifeng Chen ,  Yu Zhang ,  Yuxuan Wang ,  Russell John Wyatt Skerry-Ryan ,  Ryan M. Rifkin ,  Ioannis Agiomyrgiannakis

IPC: G10L25/30 ,  G10L13/047 ,  G10L13/08 ,  G06N7/00 ,  G06N3/08 ,  G06N3/04 ,  G06N5/04 ,  G10L25/18

Abstract: Methods, systems, and computer program products for generating, from an input character sequence, an output sequence of audio data representing the input character sequence. The output sequence of audio data includes a respective audio output sample for each of a number of time steps. One example method includes, for each of the time steps: generating a mel-frequency spectrogram for the time step by processing a representation of a respective portion of the input character sequence using a decoder neural network; generating a probability distribution over a plurality of possible audio output samples for the time step by processing the mel-frequency spectrogram for the time step using a vocoder neural network; and selecting the audio output sample for the time step from the possible audio output samples in accordance with the probability distribution.

公开(公告)号：US12175963B2

公开(公告)日：2024-12-24

申请号：US18525475

申请日：2023-11-30

Applicant: Google LLC

Inventor： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Ignacio Lopez Moreno ,  Fei Ren ,  Yu Zhang ,  Quan Wang ,  Patrick An Phu Nguyen

IPC: G10L13/04 ,  G10L17/04 ,  G10L19/00 ,  G06N3/08 ,  G10L13/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

公开(公告)号：US11823656B2

公开(公告)日：2023-11-21

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

CPC classification number: 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.

公开(公告)号：US20220351713A1

公开(公告)日：2022-11-03

申请号：US17813361

申请日：2022-07-19

Applicant: Google LLC

Inventor： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Ignacio Lopez Moreno ,  Fei Ren ,  Yu Zhang ,  Quan Wang ,  Patrick An Phu Nguyen

IPC: G10L13/04 ,  G10L17/04 ,  G10L19/00

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.

公开(公告)号：US11488575B2

公开(公告)日：2022-11-01

申请号：US17055951

申请日：2019-05-17

Applicant: Google LLC

Inventor： Ye Jia ,  Zhifeng Chen ,  Yonghui Wu ,  Jonathan Shen ,  Ruoming Pang ,  Ron J. Weiss ,  Ignacio Lopez Moreno ,  Fei Ren ,  Yu Zhang ,  Quan Wang ,  Patrick Nguyen

IPC: G10L13/04 ,  G10L17/04 ,  G10L19/00 ,  G06N3/08 ,  G10L13/02

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for speech synthesis. The methods, systems, and apparatus include actions of obtaining an audio representation of speech of a target speaker, obtaining input text for which speech is to be synthesized in a voice of the target speaker, generating a speaker vector by providing the audio representation to a speaker encoder engine that is trained to distinguish speakers from one another, generating an audio representation of the input text spoken in the voice of the target speaker by providing the input text and the speaker vector to a spectrogram generation engine that is trained using voices of reference speakers to generate audio representations, and providing the audio representation of the input text spoken in the voice of the target speaker for output.