TELKOMNIKA Telecommunication, Computing, Electronics and Control
Gender voice classification with huge accuracy rate
Dublin Core
Title
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Gender voice classification with huge accuracy rate
Gender voice classification with huge accuracy rate
Subject
Audacity, Classification accuracy, Machine learning algorithm, (J 48), MFCC, VQ
Description
Gender voice recognition stands for an imperative research field in acoustics and speech processing as human voice shows very remarkable aspects. This study investigates speech signals to devise a gender classifier by speech analysis to forecast the gender of the speaker by investigating diverse parameters of the voice sample. A database has 2270 voice samples of celebrities, both male and female. Through Mel frequency cepstrum coefficient (MFCC), vector quantization (VQ), and machine learning algorithm (J 48), an accuracy of about 100% is achieved by the proposed classification technique based on data mining and Java script.
Creator
Mustafa Sahib Shareef, Thulfiqar Abd, Yaqeen S. Mezaal
Source
DOI: 10.12928/TELKOMNIKA.v18i5.13717
Publisher
Universitas Ahmad Dahlan
Date
October 2020
Contributor
Sri Wahyuni
Rights
ISSN: 1693-6930
Relation
http://journal.uad.ac.id/index.php/TELKOMNIKA
Format
PDF
Language
English
Type
Text
Coverage
TELKOMNIKA Telecommunication, Computing, Electronics and Control
Files
Collection
Citation
Mustafa Sahib Shareef, Thulfiqar Abd, Yaqeen S. Mezaal, “TELKOMNIKA Telecommunication, Computing, Electronics and Control
Gender voice classification with huge accuracy rate,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4037.
Gender voice classification with huge accuracy rate,” Repository Horizon University Indonesia, accessed October 31, 2025, https://repository.horizon.ac.id/items/show/4037.