Algorithms for Detection Gender Using Neural Networks | Zendy

Maksat Kalimoldayev | Zendy; Оrken Mamyrbayev | Zendy; Nurbapa Mekebayev | Zendy; Aizat Kydyrbekova | Zendy

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Algorithms for Detection Gender Using Neural Networks

Author(s) -

Maksat Kalimoldayev,

Оrken Mamyrbayev,

Nurbapa Mekebayev,

Aizat Kydyrbekova

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.

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