title: Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
authors.affiliation: Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia. Electronic address: a.alnashari@yahoo.com.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Al-Nasheri
authors.affiliation: Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Ghulam
authors.initials: G
authors.lastname: Muhammad
authors.affiliation: Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Mansour
authors.initials: M
authors.lastname: Alsulaiman
authors.affiliation: Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Zulfiqar
authors.initials: Z
authors.lastname: Ali
mesh_terms: D000162:Acoustics; D016208:Databases, Factual; D006801:Humans; D010363:Pattern Recognition, Automated; D012815:Signal Processing, Computer-Assisted; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D013066:Speech-Language Pathology / Q000379:methods*; D014832:Voice Disorders / Q000145:classification / Q000175:diagnosis* / Q000503:physiopathology; D014833:Voice Quality
keywords: Arabic Voice Pathology Database (AVPD); Frequency investigation; Massachusetts Eye and Ear Infirmary (MEEI); Saarbruecken Voice Database (SVD); Voice pathology detection and classification