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.

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.

公开(公告)号：US12106749B2

公开(公告)日：2024-10-01

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: G10L15/00 ,  G06N3/08 ,  G10L15/02 ,  G10L15/06 ,  G10L15/16 ,  G10L15/22 ,  G10L25/30 ,  G10L15/26

CPC classification number: G10L15/16 ,  G06N3/08 ,  G10L15/02 ,  G10L15/063 ,  G10L15/22 ,  G10L25/30 ,  G10L2015/025 ,  G10L15/26

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.

公开(公告)号：US12094453B2

公开(公告)日：2024-09-17

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 ,  G10L15/16 ,  G10L15/187 ,  G10L15/22 ,  G10L15/30

CPC classification number: G10L15/063 ,  G10L15/16 ,  G10L15/22 ,  G10L15/30 ,  G10L15/187

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.