公开(公告)号：US20230298591A1

公开(公告)日：2023-09-21

申请号：US18123060

申请日：2023-03-17

Applicant: Google LLC

Inventor： Shaojin Ding ,  Rajeev Rikhye ,  Qiao Liang ,  Yanzhang He ,  Quan Wang ,  Arun Narayanan ,  Tom O'Malley ,  Ian McGraw

IPC: G10L17/06 ,  G10L17/22

CPC classification number: G10L17/06 ,  G10L17/22

Abstract: A computer-implemented method includes receiving a sequence of acoustic frames corresponding to an utterance and generating a reference speaker embedding for the utterance. The method also includes receiving a target speaker embedding for a target speaker and generating feature-wise linear modulation (FiLM) parameters including a scaling vector and a shifting vector based on the target speaker embedding. The method also includes generating an affine transformation output that scales and shifts the reference speaker embedding based on the FiLM parameters. The method also includes generating a classification output indicating whether the utterance was spoken by the target speaker based on the affine transformation output.

公开(公告)号：US20220310080A1

公开(公告)日：2022-09-29

申请号：US17643826

申请日：2021-12-11

Applicant: Google LLC

Inventor： David Qiu ,  Yanzhang He ,  Yu Zhang ,  Qiujia Li ,  Liangliang Cao ,  Ian McGraw

IPC: G10L15/197 ,  G10L15/06 ,  G10L15/22 ,  G10L15/02 ,  G10L15/16 ,  G10L15/30 ,  G10L15/32 ,  G10L15/04 ,  G06N3/08

Abstract: A method including receiving a speech recognition result corresponding to a transcription of an utterance spoken by a user. For each sub-word unit in a sequence of hypothesized sub-word units of the speech recognition result, using a confidence estimation module to: obtain a respective confidence embedding associated with the corresponding output step when the corresponding sub-word unit was output from the first speech recognizer; generate a confidence feature vector; generate an acoustic context vector; and generate a respective confidence output score for the corresponding sub-word unit based on the confidence feature vector and the acoustic feature vector received as input by the output layer of the confidence estimation module. The method also includes determining, based on the respective confidence output score generated for each sub-word unit in the sequence of hypothesized sub-word units, an utterance-level confidence score for the transcription of the utterance.

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.