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:
Oct. 22, 2024

Filed:

Feb. 28, 2022
Applicant:

Google Llc, Mountain View, CA (US);

Inventors:

Hyun Jin Park, Palo Alto, CA (US);

Alex Seungryong Park, Mountain View, CA (US);

Ignacio Lopez Moreno, Brooklyn, NY (US);

Assignee:

Google LLC, Mountain View, CA (US);

Attorneys:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/16 (2006.01); G06N 3/08 (2023.01); G10L 15/02 (2006.01); G10L 15/06 (2013.01); G10L 15/22 (2006.01); G06N 3/0455 (2023.01); G10L 15/08 (2006.01);
U.S. Cl.
CPC ...
G10L 15/16 (2013.01); G06N 3/08 (2013.01); G10L 15/02 (2013.01); G10L 15/063 (2013.01); G10L 15/22 (2013.01); G06N 3/0455 (2023.01); G10L 2015/025 (2013.01); G10L 2015/088 (2013.01);
Abstract

A method for training a neural network includes receiving a training input audio sequence including a sequence of input frames defining a hotword that initiates a wake-up process on a user device. The method further includes obtaining a first label and a second label for the training input audio sequence. The method includes generating, using a memorized neural network and the training input audio sequence, an output indicating a likelihood the training input audio sequence includes the hotword. The method further includes determining a first loss based on the first label and the output. The method includes determining a second loss based on the second label and the output. The method further includes optimizing the memorized neural network based on the first loss and the second loss associated with the training input audio sequence.


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