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:

Aug. 26, 2020
Applicant:

Ping an Technology (Shenzhen) Co., Ltd., Shenzhen, CN;

Inventors:

Yuechao Guo, Shenzhen, CN;

Yixuan Qiao, Shenzhen, CN;

Yijun Tang, Shenzhen, CN;

Jun Wang, Shenzhen, CN;

Peng Gao, Shenzhen, CN;

Guotong Xie, Shenzhen, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G10L 17/06 (2013.01); G10L 17/02 (2013.01); G10L 17/18 (2013.01); G10L 25/18 (2013.01); G10L 25/21 (2013.01);
U.S. Cl.
CPC ...
G10L 17/06 (2013.01); G10L 17/02 (2013.01); G10L 17/18 (2013.01); G10L 25/18 (2013.01); G10L 25/21 (2013.01);
Abstract

A method for voiceprint recognition of an original speech is used to reduce information losses and system complexity of a model for data recognition of a speaker's original speech. The method includes: obtaining original speech data, and segmenting the original speech data based on a preset time length to obtain segmented speech data; performing tail-biting convolution processing and discrete Fourier transform on the segmented speech data through a preset convolution filter bank to obtain voiceprint feature data; pooling the voiceprint feature data through a preset deep neural network to obtain a target voiceprint feature; performing embedded vector transformation on the target voiceprint feature to obtain corresponding voiceprint feature vectors; and performing calculation on the voiceprint feature vectors through a preset loss function to obtain target voiceprint data, where the loss function includes a cosine similarity matrix loss function and a minimum mean square error matrix loss function.


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