公开(公告)号：US11804212B2

公开(公告)日：2023-10-31

申请号：US17348118

申请日：2021-06-15

Applicant: Google LLC

Inventor： Thibault Doutre ,  Wei Han ,  Min Ma ,  Zhiyun Lu ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Arun Narayanan ,  Ananya Misra ,  Yu Zhang ,  Liangliang Cao

IPC: G10L15/06 ,  G10L15/08 ,  G10L15/18 ,  G06N3/04 ,  G06N3/045

CPC classification number: G10L15/063 ,  G06N3/045 ,  G10L15/083 ,  G10L15/18

Abstract: A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.

公开(公告)号：US20240029716A1

公开(公告)日：2024-01-25

申请号：US18480827

申请日：2023-10-04

Applicant: Google LLC

Inventor： Thibault Doutre ,  Wei Han ,  Min Ma ,  Zhiyun Lu ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Arun Narayanan ,  Ananya Misra ,  Yu Zhang ,  Liangliang Cao

IPC: G10L15/06 ,  G10L15/08 ,  G10L15/18 ,  G06N3/045

CPC classification number: G10L15/063 ,  G10L15/083 ,  G10L15/18 ,  G06N3/045

Abstract: A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.

公开(公告)号：US11417322B2

公开(公告)日：2022-08-16

申请号：US16712492

申请日：2019-12-12

Applicant: Google LLC

Inventor： Bhuvana Ramabhadran ,  Min Ma ,  Pedro J. Moreno Mengibar ,  Jesse Emond ,  Brian E. Roark

IPC: G10L15/19 ,  G10L15/06 ,  G10L15/16 ,  G10L15/22 ,  G06N3/08 ,  G06N3/04

Abstract: Methods, systems, and apparatus, including computer programs stored on a computer-readable storage medium, for transliteration for speech recognition training and scoring. In some implementations, language examples are accessed, some of which include words in a first script and words in one or more other scripts. At least portions of some of the language examples are transliterated to the first script to generate a training data set. A language model is generated based on occurrences of the different sequences of words in the training data set in the first script. The language model is used to perform speech recognition for an utterance.

公开(公告)号：US20220343894A1

公开(公告)日：2022-10-27

申请号：US17348118

申请日：2021-06-15

Applicant: Google LLC

Inventor： Thibault Doutre ,  Wei Han ,  Min Ma ,  Zhiyun Lu ,  Chung-Cheng Chiu ,  Ruoming Pang ,  Arun Narayanan ,  Ananya Misra ,  Yu Zhang ,  Liangliang Cao

IPC: G10L15/06 ,  G06N3/04 ,  G10L15/18 ,  G10L15/08

Abstract: A method for training a streaming automatic speech recognition student model includes receiving a plurality of unlabeled student training utterances. The method also includes, for each unlabeled student training utterance, generating a transcription corresponding to the respective unlabeled student training utterance using a plurality of non-streaming automated speech recognition (ASR) teacher models. The method further includes distilling a streaming ASR student model from the plurality of non-streaming ASR teacher models by training the streaming ASR student model using the plurality of unlabeled student training utterances paired with the corresponding transcriptions generated by the plurality of non-streaming ASR teacher models.