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:
Jun. 13, 2023

Filed:

Jan. 20, 2021
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Shuo-Yiin Chang, Sunnyvale, CA (US);

Bo Li, Fremont, CA (US);

Gabor Simko, Santa Clara, CA (US);

Maria Carolina Parada San Martin, Boulder, CO (US);

Sean Matthew Shannon, Mountain View, CA (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/16 (2006.01); G10L 25/78 (2013.01); G06N 3/08 (2023.01); G06N 20/20 (2019.01); G06N 5/046 (2023.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01);
U.S. Cl.
CPC ...
G10L 25/78 (2013.01); G06F 18/214 (2023.01); G06N 3/045 (2023.01); G06N 3/08 (2013.01); G06N 5/046 (2013.01); G06N 20/20 (2019.01); G10L 15/16 (2013.01);
Abstract

A method for training an endpointer model includes short-form speech utterances and long-form speech utterances. The method also includes providing a short-form speech utterance as input to a shared neural network, the shared neural network configured to learn shared hidden representations suitable for both voice activity detection (VAD) and end-of-query (EOQ) detection. The method also includes generating, using a VAD classifier, a sequence of predicted VAD labels and determining a VAD loss by comparing the sequence of predicted VAD labels to a corresponding sequence of reference VAD labels. The method also includes, generating, using an EOQ classifier, a sequence of predicted EOQ labels and determining an EOQ loss by comparing the sequence of predicted EOQ labels to a corresponding sequence of reference EOQ labels. The method also includes training, using a cross-entropy criterion, the endpointer model based on the VAD loss and the EOQ loss.


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