Training device of voiceprint recognition model

A voiceprint recognition and training device technology, applied in biological neural network models, speech analysis, instruments, etc., can solve problems such as complex channel differences, achieve good learning effects, and improve recall and accuracy

Active Publication Date: 2021-01-22
SOUTHWEST UNIVERSITY OF POLITICAL SCIENCE AND LAW +1
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AI Technical Summary

Problems solved by technology

[0008] Of course, due to the personal information of the speaker such as gender and dialect contained in the voice, it appears as a different frequency distribution on the spectrum

Method used

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  • Training device of voiceprint recognition model

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Embodiment Construction

[0036]Such asfigure 1As shown, a training device for a voiceprint recognition model of the present invention includes a sample collection and processing module (not shown in the figure), a feature input module 1, a feature extractor 2, a pooling layer 3, a speaker classifier 4, and a domain Classifier 5, gender classifier 6, dialect classifier 7 and optimization processing module (not shown in the figure), of which:

[0037]The sample acquisition and processing module is used to collect two-channel voice samples for voiceprint recognition and comparison training. The voice samples collected in one channel are labeled with feature labels according to the sample object, and the voice samples collected in the other channel are not Annotate feature labels, and pass the processed voice samples to feature input module 1;

[0038]The feature input module 1 is used to extract heuristic phonetic features and MFCC features from each voice sample, and merge the two to form input features and output ...

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Abstract

According to the training device of the voiceprint recognition model, the phonetic features containing the identity information of the speaker are extracted as the input features, multi-task trainingis carried out by using labels such as the gender of the speaker, the cross-channel problem is solved in combination with an adversarial training method, and finally, the stable features reflecting the identity nature of the speaker are extracted. According to the invention, the linguistic characteristics and the deep neural network are combined to simulate the learning mechanism of the human brain, so that the extraction capability, stability and interpretability of the identity essential characteristics of the speaker are improved, and finally, the accuracy and recall rate of automatic voiceprint recognition are improved.

Description

Technical field[0001]The invention relates to the field of automatic voiceprint recognition, in particular to a training device for a voiceprint recognition model for judicial voice evidence evaluation mode.Background technique[0002]In the task of speaker identity identification in the field of forensic speech, the current mainstream identification methods in China are based on several dimensions such as watching, listening, and testing, and rely on the personal experience of voiceprint experts. This method is time-consuming, labor-intensive, and contains the subjective judgment of appraisal experts, and cannot be quickly promoted in a larger group of practitioners. In addition, limited by the characteristics of this type of method, it can only be suitable for small-scale inspection materials and sample scenarios. When the inspection materials and samples to be compared are hundreds, thousands or more, voiceprint identification experts are not enough to deal with Such a huge task. F...

Claims

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Application Information

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IPC IPC(8): G10L17/00G10L17/02G10L17/04G10L17/14G10L17/18G10L25/24G10L25/69G06N3/04G06N3/08
CPCG10L17/02G10L17/04G10L17/14G10L17/18G10L25/24G10L25/69G06N3/049G06N3/08G06N3/045
Inventor 张翠玲谭铁君李稀敏杨东升叶志坚肖龙源
Owner SOUTHWEST UNIVERSITY OF POLITICAL SCIENCE AND LAW
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