Voice Disorder Detection And Classification- A Review

15 Pages Posted: 25 Nov 2020

See all articles by Roohum Jegan

Roohum Jegan

BMS College of Engineering, Bangalore

Jayagowri R

BMS College of Engineering, Bangalore

Date Written: November 21, 2020

Abstract

The speech represents an intrinsic characteristic of human behaviour. Any disturbances in the normal speech of a human being are called speech disorder. İt affects communication and social integration. Such patients will also have psychological and emotional issues as a direct result of their voice disorder. These day to day problems may cause a deterioration of the quality of life of an affected person and this results in the person trying to isolate himself from the activities of his daily life. Hence early detection of speech disorder is of utmost importance. Various conventional (invaasive) techniques for speech disorder detection were used earlier but extensive research has given rise to computer-based (non-invasive) methods of speech disorder detection. The computer-based techniques are easier to administer and are less expensive as compared to conventional methods. The important papers have been reviewed that describes the various voice disorder detection and classification algorithms from the recent years. The algorithms are divided based on the feature extraction techniques used for detection task. Various databases employed for evaluation of implemented approach are also explained. Deep learning techniques for speech pathology detection has great potential and recent studies are focussed on investigating deep learning architecture.

Keywords: Speech disorder, Voice disorder detection, classification algorithms, speech databases, feature extraction, deep learning.

Suggested Citation

Jegan, Roohum and R, Jayagowri, Voice Disorder Detection And Classification- A Review (November 21, 2020). Proceedings of the 2nd International Conference on IoT, Social, Mobile, Analytics & Cloud in Computational Vision & Bio-Engineering (ISMAC-CVB 2020), Available at SSRN: https://ssrn.com/abstract=3734762 or http://dx.doi.org/10.2139/ssrn.3734762

Roohum Jegan (Contact Author)

BMS College of Engineering, Bangalore ( email )

India

Jayagowri R

BMS College of Engineering, Bangalore ( email )

India

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