Automated Acoustic Evaluation of Voice Disorders: A Comprehensive Study on Parameter Analysis Using ANN

Hina Zameer; Sidra Abid Syed; Marium Raziq; Muhammad Muzammil Khan; Sania Tanvir; Shahzad Nasim

doi:10.62019/abbdm.v3i2.93

Authors

Hina Zameer Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.
Sidra Abid Syed Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan
Marium Raziq Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan
Muhammad Muzammil Khan Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.
Sania Tanvir Biomedical Engineering Department, Sir Syed University of Engineering and Technology, Karachi, Pakistan.
Shahzad Nasim Management Sciences &Technology Department, line 3: The Begum Nusrat Bhutto Women University, Karachi, Pakistan.

DOI:

https://doi.org/10.62019/abbdm.v3i2.93

Keywords:

voice disorder, ANN, SVD, PCA, voice disorder, ANN, SVD, PCA.

Abstract

Voice analysis serves as a crucial tool in diagnosing voice abnormalities, offering a non-invasive alternative to intrusive procedures. This research digs into a comprehensive study of voice disorder assessment methodologies, focusing on acoustic analysis and classification. The study employs various parameters such as Jitter, Shimmer, and Harmonic-to-Noise Ratio (HNR) alongside an Artificial Neural Network (ANN) classifier. Utilizing the Saarbruecken Voice Database the research aims to distinguish between healthy and dysphonic voices across genders. Principal Component Analysis (PCA) aids in feature selection, enhancing model accuracy. The results exhibit distinct precision levels in male and female groups, showcasing the effectiveness of specific parameters in classification.