Article
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DGR: Deep Gender Recognition of Human Speech
Version 1
: Received: 1 March 2019 / Approved: 4 March 2019 / Online: 4 March 2019 (13:42:02 CET)
How to cite: Alkhawaldeh, R.S. DGR: Deep Gender Recognition of Human Speech. Preprints 2019, 2019030047. https://doi.org/10.20944/preprints201903.0047.v1 Alkhawaldeh, R.S. DGR: Deep Gender Recognition of Human Speech. Preprints 2019, 2019030047. https://doi.org/10.20944/preprints201903.0047.v1
Abstract
The speech entailed in human voice comprises essentially para-linguistic information used in many voice-recognition applications. Gender voice-recognition is considered one of the pivotal parts to be detected from a given voice, a task that involves certain complications. In order to distinguish gender from a voice signal, a set of techniques have been employed to determine relevant features to be utilized for building a model from a training set. This model is useful for determining the gender (i.e, male or female) from a voice signal. The contributions are involved in two folds: (i) providing analysis information about well-known voice signal features using a prominent dataset, (ii) studying various machine learning models of different theoretical families to classify the voice gender, and (iii) using three prominent feature selection algorithms to find promisingly optimal features for improving classification models. Experimental results show the importance of sub-features over others, which are vital for enhancing the efficiency of classification models performance. Experimentation reveals that the best recall value is equal to 99.97%; 99.7% of two models of Deep Learning (DL) and Support Vector Machine (SVM) and with feature selection the best recall value is 100% for SVM techniques.
Keywords
Gender Recognition; Speech Signal; Deep Learning; Evolutionary Search; PSO search; Wolf Search
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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