J Voice. 2012 Nov;26(6):817.e19-27. doi: 10.1016/j.jvoice.2012.05.002.

Multidirectional regression (MDR)-based features for automatic voice disorder detection.

Muhammad G¹, Mesallam TA, Malki KH, Farahat M, Mahmood A, Alsulaiman M.

Author information

1: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. ghulam@ksu.edu.sa

Abstract

BACKGROUND AND OBJECTIVE:

Objective assessment of voice pathology has a growing interest nowadays. Automatic speech/speaker recognition (ASR) systems are commonly deployed in voice pathology detection. The aim of this work was to develop a novel feature extraction method for ASR that incorporates distributions of voiced and unvoiced parts, and voice onset and offset characteristics in a time-frequency domain to detect voice pathology.

MATERIALS AND METHODS:

The speech samples of 70 dysphonic patients with six different types of voice disorders and 50 normal subjects were analyzed. The Arabic spoken digits (1-10) were taken as an input. The proposed feature extraction method was embedded into the ASR system with Gaussian mixture model (GMM) classifier to detect voice disorder.

RESULTS:

Accuracy of 97.48% was obtained in text independent (all digits' training) case, and over 99% accuracy was obtained in text dependent (separate digit's training) case. The proposed method outperformed the conventional Mel frequency cepstral coefficient (MFCC) features.