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:
May. 27, 2025

Filed:

Feb. 13, 2024
Applicant:

Electronic Arts Inc., Redwood City, CA (US);

Inventors:

Shahab Raji, Foster City, CA (US);

Siddharth Gururani, Santa Clara, CA (US);

Zahra Shakeri, Newark, CA (US);

Kilol Gupta, Redwood City, CA (US);

Ping Zhong, Mountain View, CA (US);

Assignee:

ELECTRONIC ARTS INC., Redwood City, CA (US);

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 13/00 (2006.01); A63F 13/54 (2014.01); G06N 20/00 (2019.01); G10L 13/047 (2013.01); G10L 15/02 (2006.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G10L 13/047 (2013.01); A63F 13/54 (2014.09); G06N 20/00 (2019.01); G10L 15/02 (2013.01); G10L 15/16 (2013.01); A63F 2300/6081 (2013.01);
Abstract

This specification describes a computer-implemented method of training a machine-learned speech audio generation system to generate predicted acoustic features for generated speech audio for use in a video game. The training comprises receiving one or more training examples. Each training example comprises: (i) ground-truth acoustic features for speech audio, (ii) speech content data representing speech content of the speech audio, and (iii) speech expression data representing speech expression of the speech audio. Parameters of the machine-learned speech audio generation system are updated by: (i) minimizing a measure of difference between the predicted acoustic features for a training example and the corresponding ground-truth acoustic features of the training example, and (ii) minimizing a measure of difference between the predicted prosodic features for the training example and the corresponding ground-truth prosodic features for the training example.


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