Fast and Accurate Voice Biometrics with Deep Learning Algorithm of CNN Depthwise Separable Convolution Model and Fusion of DWT-MFCC Methods

Abstract

Theft of private data became a threat of crime in cyberspace. This issue was in line with rapid development of data technology, especially online transactions. To attenuate this problem, voice biometrics was developed as an answer to keep up security identity. This paper develops the voice biometric framework based on Convolutional Neural Network Depthwise Separable Convolution (DSC) model and the fusion of Discrete Wavelet Transform (DWT) and Mel Frequency Cepstral Coefficients (MFCC). Such a scheme has targeted to increase the high accuracy, to reduce the burden of high computational costs and to speed up the performance of classification process time. We conduct three testing performance, i.e. voice Biometric Training Performance, speaker Recognition Performance (”Who is speaking?”), and Speech Recognition performance (”What keyword is uttered?”).  For each of the testing, the results are compared with CNN Standard performance. The training results has shown that CNN DSC model has reduced the amount of training parameters to 364,506, leading to accelerate the performance of training process time to 5.12 minutes. The results of speaker recognition performance has attained the best performance with an accuracy 99.25%, precision 97.14%, recall 98.17% and F1-score 97.28%. The results of speech recognition performance has been able to improve the best performance with accuracy 100%. It can be concluded that CNN DSC with the fusion of DWT- MFCC has outperformed the CNN Standard. The framework can be applied for the identification and verification of user voices accurately, quickly and efficiently for any applications requiring better security performance.