Neural architectures for gender detection and speaker identification

Neural architectures for gender detection and speaker identification

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-laye...

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Bibliographic Details
Main Authors: Orken Mamyrbayev, Alymzhan Toleu, Gulmira Tolegen, Nurbapa Mekebayev
Format: Article
Language:English
Published: Taylor & Francis Group 2020-01-01
Series:Cogent Engineering
Subjects:
mlp
cnn
gender detection
speaker identification
Online Access:http://dx.doi.org/10.1080/23311916.2020.1727168

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