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. 24, 2023

Filed:

Oct. 08, 2019
Applicant:

Nippon Telegraph and Telephone Corporation, Tokyo, JP;

Inventors:

Atsushi Ando, Tokyo, JP;

Hosana Kamiyama, Tokyo, JP;

Satoshi Kobashikawa, Tokyo, JP;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/00 (2013.01); G10L 25/63 (2013.01); G10L 15/02 (2006.01); G10L 15/16 (2006.01);
U.S. Cl.
CPC ...
G10L 25/63 (2013.01); G10L 15/02 (2013.01); G10L 15/16 (2013.01);
Abstract

To increase the accuracy of paralinguistic information estimation. A paralinguistic information estimation model storage unitstores a paralinguistic information estimation model outputting, with a plurality of independent features as inputs, paralinguistic information estimation results. A feature extraction unitextracts the features from an input utterance. A paralinguistic information estimation unitestimates paralinguistic information of the input utterance from the features extracted from the input utterance, by using the paralinguistic information estimation model. The paralinguistic information estimation model includes, for each of the features, a feature sub-model outputting information to be used for estimation of paralinguistic information, based only on the feature, for each of the features, a feature weight calculation unit calculating a feature weight, based on an output result of the feature sub-model, for each of the features, a feature gate weighting the output result from the feature sub-model with the feature weight and outputting a result, and a result integration sub-model estimating the paralinguistic information, based on output results from all the feature gates.


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