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:
Aug. 31, 2021

Filed:

Jul. 02, 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 15/06 (2013.01); G10L 15/02 (2006.01); G10L 15/22 (2006.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G10L 15/063 (2013.01); G10L 15/02 (2013.01); G10L 15/22 (2013.01); G10L 15/16 (2013.01); G10L 2015/025 (2013.01);
Abstract

Embodiments are associated with a speaker-independent acoustic model capable of classifying senones based on input speech frames and on first parameters of the speaker-independent acoustic model, a speaker-dependent acoustic model capable of classifying senones based on input speech frames and on second parameters of the speaker-dependent acoustic model, and a discriminator capable of receiving data from the speaker-dependent acoustic model and data from the speaker-independent acoustic model and outputting a prediction of whether received data was generated by the speaker-dependent acoustic model based on third parameters. The second parameters are initialized based on the first parameters, the second parameters are trained based on input frames of a target speaker to minimize a senone classification loss associated with the second parameters, a portion of the second parameters are trained based on the input frames of the target speaker to maximize a discrimination loss associated with the discriminator, and the third parameters are trained based on the input frames of the target speaker to minimize the discrimination loss.


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