公开(公告)号：US20240153495A1

公开(公告)日：2024-05-09

申请号：US18494984

申请日：2023-10-26

Applicant: Google LLC

Inventor： Weiran Wang ,  Ding Zhao ,  Shaojin Ding ,  Hao Zhang ,  Shuo-yiin Chang ,  David Johannes Rybach ,  Tara N. Sainath ,  Yanzhang He ,  Ian McGraw ,  Shankar Kumar

IPC: G10L15/06 ,  G06F40/284 ,  G10L15/26

CPC classification number: G10L15/063 ,  G06F40/284 ,  G10L15/26

Abstract: A method includes receiving a training dataset that includes one or more spoken training utterances for training an automatic speech recognition (ASR) model. Each spoken training utterance in the training dataset paired with a corresponding transcription and a corresponding target sequence of auxiliary tokens. For each spoken training utterance, the method includes generating a speech recognition hypothesis for a corresponding spoken training utterance, determining a speech recognition loss based on the speech recognition hypothesis and the corresponding transcription, generating a predicted auxiliary token for the corresponding spoken training utterance, and determining an auxiliary task loss based on the predicted auxiliary token and the corresponding target sequence of auxiliary tokens. The method also includes the ASR model jointly on the speech recognition loss and the auxiliary task loss determined for each spoken training utterance.

公开(公告)号：US11545142B2

公开(公告)日：2023-01-03

申请号：US16827937

申请日：2020-03-24

Applicant: Google LLC

Inventor： Ding Zhao ,  Bo Li ,  Ruoming Pang ,  Tara N. Sainath ,  David Rybach ,  Deepti Bhatia ,  Zelin Wu

IPC: G10L15/183 ,  G10L15/16 ,  G06N20/00 ,  G06K9/62 ,  G06N3/08

Abstract: A method includes receiving audio data encoding an utterance, processing, using a speech recognition model, the audio data to generate speech recognition scores for speech elements, and determining context scores for the speech elements based on context data indicating a context for the utterance. The method also includes executing, using the speech recognition scores and the context scores, a beam search decoding process to determine one or more candidate transcriptions for the utterance. The method also includes selecting a transcription for the utterance from the one or more candidate transcriptions.

公开(公告)号：US20230326461A1

公开(公告)日：2023-10-12

申请号：US18182925

申请日：2023-03-13

Applicant: Google LLC

Inventor： Shaojin Ding ,  Yangzhang He ,  Xin Wang ,  Weiran Wang ,  Trevor Strohman ,  Tara N. Sainath ,  Rohit Parkash Prabhavalkar ,  Robert David ,  Rina Panigrahy ,  Rami Botros ,  Qiao Liang ,  Ian Mcgraw ,  Ding Zhao ,  Dongseong Hwang

IPC: G10L15/32 ,  G10L15/16 ,  G10L15/22

CPC classification number: G10L15/32 ,  G10L15/16 ,  G10L15/22 ,  G10L2015/223

Abstract: An automated speech recognition (ASR) model includes a first encoder, a first encoder, a second encoder, and a second decoder. The first encoder receives, as input, a sequence of acoustic frames, and generates, at each of a plurality of output steps, a first higher order feature representation for a corresponding acoustic frame in the sequence of acoustic frames. The first decoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a first probability distribution over possible speech recognition hypotheses. The second encoder receives, as input, the first higher order feature representation generated by the first encoder, and generates a second higher order feature representation for a corresponding first higher order feature frame. The second decoder receives, as input, the second higher order feature representation generated by the second encoder, and generates a second probability distribution over possible speech recognition hypotheses.