The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 15, 2026

Filed:

Feb. 28, 2024
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Andrew M. Rosenberg, Brooklyn, NY (US);

Murali Karthick Baskar, Mountain View, CA (US);

Bhuvana Ramabhadran, Mt. Kisco, NY (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G10L 15/06 (2013.01); G10L 15/01 (2013.01); G10L 15/16 (2006.01); G10L 15/197 (2013.01);
U.S. Cl.
CPC ...
G10L 15/063 (2013.01); G10L 15/01 (2013.01); G10L 15/16 (2013.01); G10L 15/197 (2013.01);
Abstract

A method includes, for each training sample of a plurality of training samples, processing, using an RNN-T model, a corresponding sequence of acoustic frames to obtain an n-best list of speech recognition hypotheses, and, for each speech recognition hypothesis of the n-best list, determining a corresponding number of word errors relative to a corresponding ground-truth transcription. For a top-ranked hypothesis from the n-best list, the method includes determining a first loss based on the corresponding ground-truth transcription. The method includes identifying, as an oracle hypothesis, the speech recognition hypothesis from the n-best list having the smallest corresponding number of word errors relative to the corresponding ground-truth transcription, and determining a second loss for the oracle hypothesis based on the corresponding ground-truth transcription. The method includes determining a corresponding self-training combined loss based on the first and second losses, and training the model based on the corresponding self-training combined loss.


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