Open AccessAlgorithms for Detection Gender Using Neural Networks
Publication year - 2020
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2020.14.24
Subject(s) - mel frequency cepstrum , computer science , convolutional neural network , artificial neural network , multilayer perceptron , artificial intelligence , pattern recognition (psychology) , speech recognition , perceptron , cover (algebra) , machine learning , feature extraction , engineering , mechanical engineering
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.