title: Multidirectional regression (MDR)-based features for automatic voice disorder detection.
abstract: 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.
  
  
  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.
  
  
  CONCLUSIONS
  The results of this study revealed that incorporating voice onset and offset information leads to efficient automatic voice disordered detection.
authors.affiliation: Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. ghulam@ksu.edu.sa
authors.forename: Ghulam
authors.initials: G
authors.lastname: Muhammad
authors.forename: Tamer A
authors.initials: TA
authors.lastname: Mesallam
authors.forename: Khalid H
authors.initials: KH
authors.lastname: Malki
authors.forename: Mohamed
authors.initials: M
authors.lastname: Farahat
authors.forename: Awais
authors.initials: A
authors.lastname: Mahmood
authors.forename: Mansour
authors.initials: M
authors.lastname: Alsulaiman
mesh_terms: D000162:Acoustics; D000293:Adolescent; D000328:Adult; D000465:Algorithms; D001331:Automation; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D012815:Signal Processing, Computer-Assisted; D013018:Sound Spectrography; D013061:Speech Acoustics; D013068:Speech Production Measurement; D013997:Time Factors; D014832:Voice Disorders / Q000175:diagnosis* / Q000503:physiopathology; D014833:Voice Quality; D055815:Young Adult