Ahmed Al-Nasheri1, Ghulam Muhammad2, Mansour Alsulaiman1, Zulfiqar Ali3, Tamer A Mesallam4, Mohamed Farahat5, Khalid H Malki5, Mohamed A Bencherif1. 1. Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. 2. Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia. Electronic address: ghulam@ksu.edu.sa. 3. Digital Speech Processing Group, Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia; Centre for Intelligent Signal and Imaging Research (CISIR), Department of Electrical and Electronic Engineering, Universiti Tekhnologi PETRONAS, Tronoh, Perak 31750, Malaysia. 4. ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia; Research Chair of Voice, Swallowing, and Communication Disorders, King Saud University, Riyadh, Saudi Arabia; ENT Department, College of Medicine, Al-Menoufiya University, Shebin Alkoum, Egypt. 5. ENT Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia; Research Chair of Voice, Swallowing, and Communication Disorders, King Saud University, Riyadh, Saudi Arabia.
Abstract
BACKGROUND AND OBJECTIVE: Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes. MATERIALS AND METHODS: Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples. RESULTS: The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively.
BACKGROUND AND OBJECTIVE: Automatic voice-pathology detection and classification systems may help clinicians to detect the existence of any voice pathologies and the type of pathology from which patients suffer in the early stages. The main aim of this paper is to investigate Multidimensional Voice Program (MDVP) parameters to automatically detect and classify the voice pathologies in multiple databases, and then to find out which parameters performed well in these two processes. MATERIALS AND METHODS: Samples of the sustained vowel /a/ of normal and pathological voices were extracted from three different databases, which have three voice pathologies in common. The selected databases in this study represent three distinct languages: (1) the Arabic voice pathology database; (2) the Massachusetts Eye and Ear Infirmary database (English database); and (3) the Saarbruecken Voice Database (German database). A computerized speech lab program was used to extract MDVP parameters as features, and an acoustical analysis was performed. The Fisher discrimination ratio was applied to rank the parameters. A t test was performed to highlight any significant differences in the means of the normal and pathological samples. RESULTS: The experimental results demonstrate a clear difference in the performance of the MDVP parameters using these databases. The highly ranked parameters also differed from one database to another. The best accuracies were obtained by using the three highest ranked MDVP parameters arranged according to the Fisher discrimination ratio: these accuracies were 99.68%, 88.21%, and 72.53% for the Saarbruecken Voice Database, the Massachusetts Eye and Ear Infirmary database, and the Arabic voice pathology database, respectively.
Authors: Ghulam Muhammad; Mohammed F Alhamid; M Shamim Hossain; Ahmad S Almogren; Athanasios V Vasilakos Journal: Sensors (Basel) Date: 2017-01-29 Impact factor: 3.576