title: Intra- and Inter-database Study for Arabic, English, and German Databases: Do Conventional Speech Features Detect Voice Pathology?
abstract: A large population around the world has voice complications. Various approaches for subjective and objective evaluations have been suggested in the literature. The subjective approach strongly depends on the experience and area of expertise of a clinician, and human error cannot be neglected. On the other hand, the objective or automatic approach is noninvasive. Automatic developed systems can provide complementary information that may be helpful for a clinician in the early screening of a voice disorder. At the same time, automatic systems can be deployed in remote areas where a general practitioner can use them and may refer the patient to a specialist to avoid complications that may be life threatening. Many automatic systems for disorder detection have been developed by applying different types of conventional speech features such as the linear prediction coefficients, linear prediction cepstral coefficients, and Mel-frequency cepstral coefficients (MFCCs). This study aims to ascertain whether conventional speech features detect voice pathology reliably, and whether they can be correlated with voice quality. To investigate this, an automatic detection system based on MFCC was developed, and three different voice disorder databases were used in this study. The experimental results suggest that the accuracy of the MFCC-based system varies from database to database. The detection rate for the intra-database ranges from 72% to 95%, and that for the inter-database is from 47% to 82%. The results conclude that conventional speech features are not correlated with voice, and hence are not reliable in pathology detection.
authors.affiliation: Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia; Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Perak, Malaysia. Electronic address: zuali@ksu.edu.sa.
authors.forename: Zulfiqar
authors.initials: Z
authors.lastname: Ali
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: Ghulam
authors.initials: G
authors.lastname: Muhammad
authors.affiliation: Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Teknologi PETRONAS, Perak, Malaysia.
authors.forename: Irraivan
authors.initials: I
authors.lastname: Elamvazuthi
authors.affiliation: Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Ahmed
authors.initials: A
authors.lastname: Al-Nasheri
authors.affiliation: ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia; ENT Department, College of Medicine, Al-Menoufiya University, Shebin Alkoum, Egypt.
authors.forename: Tamer A
authors.initials: TA
authors.lastname: Mesallam
authors.affiliation: ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Mohamed
authors.initials: M
authors.lastname: Farahat
authors.affiliation: ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
authors.forename: Khalid H
authors.initials: KH
authors.lastname: Malki
mesh_terms: D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D007802:Language; D010363:Pattern Recognition, Automated; D011237:Predictive Value of Tests; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D013061:Speech Acoustics; D013068:Speech Production Measurement / Q000379:methods*; D014832:Voice Disorders / Q000175:diagnosis* / Q000503:physiopathology; D014833:Voice Quality
keywords: GMM; Inter-database; Intra-database; MFCC; Voice disorder detection