Neural architectures for gender detection and speaker identification

Abstract

In this paper, we investigate two neural architecture for gender detection and speaker identification tasks by utilizing Mel-frequency cepstral coefficients (MFCC) features which do not cover the voice related characteristics. One of our goals is to compare different neural architectures, multi-layers perceptron (MLP) and, convolutional neural networks (CNNs) for both tasks with various settings and learn the gender/speaker-specific features automatically. The experimental results reveal that the models using z-score and Gramian matrix transformation obtain better results than the models only use max-min normalization of MFCC. In terms of training time, MLP requires large training epochs to converge than CNN. Other experimental results show that MLPs outperform CNNs for both tasks in terms of generalization errors.