TITLE:
Adaptive Threshold Estimation of Open Set Voiceprint Recognition Based on OTSU and Deep Learning
AUTHORS:
Xudong Li, Xinjia Yang, Linhua Zhou
KEYWORDS:
Voiceprint Recognition, Deep Neural Network (DNN), OTSU, Adaptive Threshold
JOURNAL NAME:
Journal of Applied Mathematics and Physics,
Vol.8 No.11,
November
30,
2020
ABSTRACT: Aiming at the problem of open set voiceprint recognition, this paper proposes an adaptive threshold algorithm based on OTSU and deep learning. The bottleneck technology of open set voiceprint recognition lies in the calculation of similarity values and thresholds of speakers inside and outside the set. This paper combines deep learning and machine learning methods, and uses a Deep Belief Network stacked with three layers of Restricted Boltzmann Machines to extract deep voice features from basic acoustic features. And by training the Gaussian Mixture Model, this paper calculates the similarity value of the feature, and further determines the threshold of the similarity value of the feature through OTSU. After experimental testing, the algorithm in this paper has a false rejection rate of 3.00% for specific speakers, a false acceptance rate of 0.35% for internal speakers, and a false acceptance rate of 0 for external speakers. This improves the accuracy of traditional methods in open set voiceprint recognition. This proves that the method is feasible and good recognition effect.