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:
May. 21, 2024

Filed:

Oct. 20, 2021
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Zhehuai Chen, Jersey City, NJ (US);

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

Andrew Rosenberg, Brooklyn, NY (US);

Yu Zhang, Mountain View, CA (US);

Pedro J. Moreno Mengibar, Jersey City, NJ (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorneys:
Primary Examiner:
Assistant Examiner:
Int. Cl.
CPC ...
G10L 13/047 (2013.01); G10L 13/08 (2013.01); G10L 13/10 (2013.01);
U.S. Cl.
CPC ...
G10L 13/047 (2013.01); G10L 13/086 (2013.01); G10L 13/10 (2013.01);
Abstract

A method for training a speech recognition model includes obtaining a multilingual text-to-speech (TTS) model. The method also includes generating a native synthesized speech representation for an input text sequence in a first language that is conditioned on speaker characteristics of a native speaker of the first language. The method also includes generating a cross-lingual synthesized speech representation for the input text sequence in the first language that is conditioned on speaker characteristics of a native speaker of a different second language. The method also includes generating a first speech recognition result for the native synthesized speech representation and a second speech recognition result for the cross-lingual synthesized speech representation. The method also includes determining a consistent loss term based on the first speech recognition result and the second speech recognition result and updating parameters of the speech recognition model based on the consistent loss term.


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