Research Repository

Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions.

Al-Nasheri, Ahmed and Muhammad, Ghulam and Alsulaiman, Mansour and Ali, Zulfiqar (2017) 'Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions.' Journal of Voice, 31 (1). 3 - 15. ISSN 0892-1997

[img]
Preview
Text
2015_Correlation Functions_accepted.pdf - Accepted Version

Download (622kB) | Preview

Abstract

Automatic voice pathology detection and classification systems effectively contribute to the assessment of voice disorders, which helps clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. This work concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using correlation functions. In this paper, we extracted maximum peak values and their corresponding lag values from each frame of a voiced signal by using correlation functions as features to detect and classify pathological samples. These features are investigated in different frequency bands to see the contribution of each band on the detection and classification processes.Various samples of sustained vowel /a/ of normal and pathological voices were extracted from three different databases: English, German, and Arabic. A support vector machine was used as a classifier. We also performed a t test to investigate the significant differences in mean of normal and pathological samples.The best achieved accuracies in both detection and classification were varied depending on the band, the correlation function, and the database. The most contributive bands in both detection and classification were between 1000 and 8000 Hz. In detection, the highest acquired accuracies when using cross-correlation were 99.809%, 90.979%, and 91.168% in the Massachusetts Eye and Ear Infirmary, Saarbruecken Voice Database, and Arabic Voice Pathology Database databases, respectively. However, in classification, the highest acquired accuracies when using cross-correlation were 99.255%, 98.941%, and 95.188% in the three databases, respectively.

Item Type: Article
Uncontrolled Keywords: Humans, Voice Disorders, Speech Production Measurement, Speech-Language Pathology, Voice Quality, Speech Acoustics, Acoustics, Signal Processing, Computer-Assisted, Databases, Factual, Pattern Recognition, Automated
Divisions: Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User: Elements
Date Deposited: 09 Apr 2020 14:11
Last Modified: 09 Apr 2020 15:15
URI: http://repository.essex.ac.uk/id/eprint/27219

Actions (login required)

View Item View Item