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:
Nov. 09, 2021

Filed:

Jul. 26, 2019
Applicant:

Microsoft Technology Licensing, Llc, Redmond, WA (US);

Inventors:

Zhong Meng, Seattle, WA (US);

Jinyu Li, Redmond, WA (US);

Yifan Gong, Sammamish, WA (US);

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 17/18 (2013.01); G10L 15/06 (2013.01); G10L 15/02 (2006.01); G10L 17/04 (2013.01); G10L 15/16 (2006.01); G06N 3/04 (2006.01);
U.S. Cl.
CPC ...
G10L 17/18 (2013.01); G06N 3/0454 (2013.01); G10L 15/02 (2013.01); G10L 15/063 (2013.01); G10L 15/16 (2013.01); G10L 17/04 (2013.01);
Abstract

To generate substantially domain-invariant and speaker-discriminative features, embodiments are associated with a feature extractor to receive speech frames and extract features from the speech frames based on a first set of parameters of the feature extractor, a senone classifier to identify a senone based on the received features and on a second set of parameters of the senone classifier, an attention network capable of determining a relative importance of features extracted by the feature extractor to domain classification, based on a third set of parameters of the attention network, a domain classifier capable of classifying a domain based on the features and the relative importances, and on a fourth set of parameters of the domain classifier; and a training platform to train the first set of parameters of the feature extractor and the second set of parameters of the senone classifier to minimize the senone classification loss, train the first set of parameters of the feature extractor to maximize the domain classification loss, and train the third set of parameters of the attention network and the fourth set of parameters of the domain classifier to minimize the domain classification loss.


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