Deep Learning-based Analysis of Voiceprint Data Mining

Abstract

: In the information age, the intelligent data mining method represented by deep learning is playing an important role in various fields at present. It is necessary to study how to efficiently use the intelligent data mining method to obtain valuable information from massive information. Open-set voiceprint recognition is realized by intelligent data mining technology. Therefore, it is of great practical significance to achieve rapid and accurate identification of the speaker's identity. Because the traditional voiceprint recognition method has insufficient ability to distinguish the speakers inside and outside the set, it often leads to a high false recognition rate. Mining parameters containing more speakers’ personality characteristics and how to calculate the threshold become the bottleneck problems of open set voiceprint recognition. Therefore, this paper adopts the deep confidence network stacked by three layers of restricted Boltzmann machines as the deep acoustic feature extractor. The mel-frequency cepstral coefficients of 24-dimensional basic acoustic features are mapped to 256-dimensional feature space, and the parameters of deep acoustic features containing more speaker's personality characteristics are obtained. Then, an open-set adaptive threshold calculation algorithm is obtained. In this paper, the similarity value of deep acoustic features is calculated by the Gaussian mixture model, and the maximum inter-class variance of the similarity value is calculated by the OTSU algorithm. When the inter-class variance is the maximum, the similarity value is the best threshold. The experimental test shows that the algorithm for calculating threshold based on deep learning proposed in this paper has a lower false rejection rate and lower false rejection rate.