公开(公告)号：US20240233707A9

公开(公告)日：2024-07-11

申请号：US18488578

申请日：2023-10-17

Applicant: Google LLC

Inventor： Tien-Ju Yang ,  You-Chi Cheng ,  Shankar Kumar ,  Jared Lichtarge ,  Ehsan Amid ,  Yuxin Ding ,  Rajiv Mathews ,  Mingqing Chen

IPC: G10L15/06 ,  G10L15/197 ,  G10L15/30

CPC classification number: G10L15/063 ,  G10L15/197 ,  G10L15/30 ,  G10L2015/0635

Abstract: A method includes receiving distillation data including a plurality of out-of-domain training utterances. For each particular out-of-domain training utterance of the distillation data, the method includes generating a corresponding augmented out-of-domain training utterance, and generating, using a teacher ASR model trained on training data corresponding to a target domain, a pseudo-label corresponding to the corresponding augmented out-of-domain training utterance. The method also includes distilling a student ASR model from the teacher ASR model by training the student ASR model using the corresponding augmented out-of-domain training utterances paired with the corresponding pseudo-labels generated by the teacher ASR model.

公开(公告)号：US20240135918A1

公开(公告)日：2024-04-25

申请号：US18488578

申请日：2023-10-16

Applicant: Google LLC

Inventor： Tien-Ju Yang ,  You-Chi Cheng ,  Shankar Kumar ,  Jared Lichtarge ,  Ehsan Amid ,  Yuxin Ding ,  Rajiv Mathews ,  Mingqing Chen

IPC: G10L15/06 ,  G10L15/197 ,  G10L15/30

CPC classification number: G10L15/063 ,  G10L15/197 ,  G10L15/30 ,  G10L2015/0635

Abstract: A method includes receiving distillation data including a plurality of out-of-domain training utterances. For each particular out-of-domain training utterance of the distillation data, the method includes generating a corresponding augmented out-of-domain training utterance, and generating, using a teacher ASR model trained on training data corresponding to a target domain, a pseudo-label corresponding to the corresponding augmented out-of-domain training utterance. The method also includes distilling a student ASR model from the teacher ASR model by training the student ASR model using the corresponding augmented out-of-domain training utterances paired with the corresponding pseudo-labels generated by the teacher ASR model.