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:
Dec. 02, 2025

Filed:

Oct. 20, 2023
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Tara N. Sainath, Jersey City, NJ (US);

Rohit Prakash Prabhavalkar, Palo Alto, CA (US);

Diamantino Antonio Caseiro, Philadelphia, PA (US);

Patrick Maxim Rondon, New York, NY (US);

Cyril Allauzen, Mountain View, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/16 (2006.01); G10L 15/06 (2013.01); G10L 15/183 (2013.01);
U.S. Cl.
CPC ...
G10L 15/16 (2013.01); G10L 15/063 (2013.01); G10L 15/183 (2013.01);
Abstract

A method includes receiving context biasing data that includes a set of unspoken textual utterances corresponding to a particular context. The method also includes obtaining a list of carrier phrases associated with the particular context. For each respective unspoken textual utterance, the method includes generating a corresponding training data pair that includes the respective unspoken textual utterance and a carrier phrase. For each respective training data pair, the method includes tokenizing the respective training data pair into a sequence of sub-word units, generating a first higher order textual feature representation for a corresponding sub-word unit, receiving the first higher order textual feature representation, and generating a first probability distribution over possible text units. The method also includes training a speech recognition model based on the first probability distribution over possible text units.


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