Voiceprint discrimination method and device, computer equipment and storage medium

Abstract

The invention relates to a voiceprint discrimination method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a first quality parameter group of detected material data, and acquiring a target correlation coefficient; determining a target preset threshold value based on a first preset model and the first quality parameter group of the detected material data; and judging the target correlation coefficient based on the target preset threshold value. The voice quality of the voice training sample is matched with the voice quality of the detected material data, so that the first preset model obtained by training based on the voice training sample can be associated with the voice quality of the detected material data, and the association can be reflected to the output target preset threshold value. Therefore, the preset threshold value can be associated with the voice quality of the detected material data, can correspondingly change along with the change of the voice quality of the detected material data, and is not a fixed value any more, so that the subjectivity during voiceprint homology judgment can be greatly reduced, and the accuracy during voiceprint homology judgment can be improved.

Description

technical field [0001] The present application relates to the technical field of voice recognition, in particular to a voiceprint discrimination method, device, computer equipment and storage medium. Background technique [0002] With the rapid development of voiceprint recognition technology, voiceprint comparison is now mainly based on deep learning methods. Specifically, the appraiser will input the inspection material data that needs to be compared into the deep learning system, and the system will compare the inspection material data with the numerous sample data collected in the system one by one, and the system will perform correlation analysis on each pair of data, And give the score of the similarity index (usually the distance evaluation of the voiceprint feature vector). The larger the score, the greater the possibility of homology between the two; otherwise, the greater the possibility of heterology. In an actual deep learning system, a threshold (usually an ex...

AI Technical Summary

Problems solved by technology

Since the external environment and channel may be different each time the data of the evidence is collected, a fixed threshold is always used to compare with the score of the similarity index, and misjudgment may occur because the threshold is set too small. Or misjudgment occurs due to excessive threshold setting, which seriously affects the accuracy of identification results

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