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:
Feb. 10, 2026

Filed:

Dec. 13, 2023
Applicant:

Inner Mongolia University, Hohhot, CN;

Inventors:

Rui Liu, Hohhot, CN;

Jinhua Zhang, Hohhot, CN;

Yifan Hu, Hohhot, CN;

Haolin Zuo, Hohhot, CN;

Yuxuan MA, Hohhot, CN;

Assignee:
Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 15/00 (2013.01); G10L 17/04 (2013.01); G10L 25/18 (2013.01); G10L 25/51 (2013.01);
U.S. Cl.
CPC ...
G10L 25/51 (2013.01); G10L 17/04 (2013.01); G10L 25/18 (2013.01);
Abstract

A fake voice detection method based on dual-track differential modeling includes: converting, by a pre-trained single/dual-track voice conversion model, a single-track audio into a stereo; extracting left and right-track Mel spectrogram features; performing texture enhancement on absolute values of differences between an original single-track Mel spectrogram feature and each of the left and right-track Mel spectrogram features; inputting texture enhancement results into left and right dual-branch feature extractors respectively to acquire a final attention map through feature extraction, processing and fusion; and inputting the final attention map into an attention pooling layer and a final binary classification layer to acquire a fake voice detection result. The fake voice detection method achieves fake voice detection by means of dual-track differential modeling, fine-grained texture enhancement, and multi-head attention feature fusion, improving the accuracy, transferability, and generalization of fake voice detection.


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