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：US11922932B2

公开(公告)日：2024-03-05

申请号：US18194586

申请日：2023-03-31

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 ,  G10L15/02 ,  G10L15/06 ,  G10L15/16 ,  G10L15/22

CPC classification number: G10L15/197 ,  G10L15/02 ,  G10L15/063 ,  G10L15/16 ,  G10L15/22 ,  G10L2015/025

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 a set of speech recognition hypothesis samples, 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.

公开(公告)号：US20220005465A1

公开(公告)日：2022-01-06

申请号：US17448119

申请日：2021-09-20

Applicant: Google LLC

Inventor： Rohit Prakash Prabhavalkar ,  Zhifeng Chen ,  Bo Li ,  Chung-cheng Chiu ,  Kanury Kanishka Rao ,  Yonghui Wu ,  Ron J. Weiss ,  Navdeep Jaitly ,  Michiel A.u. Bacchiani ,  Tara N. Sainath ,  Jan Kazimierz Chorowski ,  Anjuli Patricia Kannan ,  Ekaterina Gonina ,  Patrick An Phu Nguyen

IPC: G10L15/16 ,  G10L15/22 ,  G10L15/02 ,  G06N3/08 ,  G10L15/06 ,  G10L25/30

Abstract: A method for performing speech recognition using sequence-to-sequence models includes receiving audio data for an utterance and providing features indicative of acoustic characteristics of the utterance as input to an encoder. The method also includes processing an output of the encoder using an attender to generate a context vector, generating speech recognition scores using the context vector and a decoder trained using a training process, and generating a transcription for the utterance using word elements selected based on the speech recognition scores. The transcription is provided as an output of the ASR system.