Research on Voiceprint Recognition Technology Based on Deep Neural Network

Abstract

In recent years, the technology of identity authentication based on biometrics is constantly improving and maturing. Due to the unique advantages of long-distance and multi equipment data acquisition, voiceprint recognition has been gradually commercialized in the past fifty years. However, the traditional voiceprint recognition method will reduce the model performance under the condition of large-scale data. In view of the above problem, this paper mainly studies text independent speaker verification system, using Mel Frequency Cepstrum Coefficient (MFCC) as speech feature parameter based on deep neural network (DNN). Compared with the traditional neural network, this method has the fast-learning ability of the network weight and high recognition rate. In this experiment, different speed speech is added to the training, which improves the recognition accuracy, and promotes the robustness of the model.