Voice biometrics: Deep learning-based voiceprint authentication system

Abstract

Speaker identification systems are becoming more important in today's world. This is especially true as devices rely on the user to speak commands. In this article, an analysis of how a text-independent voice identification system can be built is presented. Extracting the Mel-Frequency Cepstral Coefficients is evaluated and a support vector machine is trained and tested on two different data sets, one from LibriSpeech and one from in-house recorded audio files. The results show the ability for such systems to be utilized in both speaker identification and speaker verification tasks.