Abstract
In this paper, we investigate two neural architecture for gender detection 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 -specific features automatically.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have