IMPLEMENTATION OF HUMAN VOICE-BASED GENDER CLASSIFICATION USING DEEP LEARNING CONVOLUTIONAL NEURAL NETWORK
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Abstract
Over the previous years, a marvelous quantity of study was performed by
utilizing the artificial intelligence based deep learning approaches for the gender
recognition applications. The gender recognition facing the problems in as
preprocessing, feature extraction and classification stages mostly through the speech
input signals, thus solving these problems is mandatory to improve the classification
accuracy of speech processing. To provide the prominent solution, this paper focuses
on investigation of various speech recognition methodologies developed by the
various researches in the past few years. Initially, spectrum subtraction method is used
to perform the preprocessing of speech signal. Then, MFCC features are extracted
from speech signal and tested with the Deep learning convolutional neural network
(DLCNN) model for classifying the gender. The extensive simulation results shows
that the proposed method gives the better classification accuracy compared to the state
of art approaches.
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