Voice-Based Gender Identification Using Machine Learning

Clicks: 1
ID: 312739
2025
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #505 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Automatic gender classification (AGC) based on voice signals plays a crucial role in biometric authentication, speech analytics, and human-computer interaction. This study proposes a hybrid machine learning framework that integrates Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction, Principal Component Analysis (PCA) for dimensionality reduction, and a Convolutional Neural Network (CNN) for classification. The model was trained on a curated dataset of 3,497 Urdu-language voice samples collected from publicly available YouTube recordings and processed for gender classification tasks, encompassing speakers of varying genders and dialects. Addressing limitations in prior approaches, the proposed method combines traditional spectral features with deep learning techniques to enhance classification performance. The system achieved an accuracy of 98.4%, along with strong precision, recall, and F1-score metrics, outperforming baseline models such as Support Vector Machines (SVM) and k-Nearest Neighbours (KNN). These findings support the model’s applicability in real-world use cases, including virtual assistants, automated call routing, and emotion-aware computing systems.
Reference Key
imported_1777056393_69ebba891036a Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Umair Ijaz, Muhammad Munwar Iqbal, Ze Shan Ali, Anees Tariq, Romail Khan
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/955
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.