Z
Zulfiqar Ali
Researcher at King Saud University
Publications - 58
Citations - 1704
Zulfiqar Ali is an academic researcher from King Saud University. The author has contributed to research in topics: Voice Disorder & Digital watermarking. The author has an hindex of 21, co-authored 54 publications receiving 1206 citations. Previous affiliations of Zulfiqar Ali include Ulster University & Petronas.
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Journal ArticleDOI
Development of the Arabic Voice Pathology Database and Its Evaluation by Using Speech Features and Machine Learning Algorithms
Tamer A. Mesallam,Mohamed Farahat,Khalid H. Malki,Mansour Alsulaiman,Zulfiqar Ali,Ahmed Al-nasheri,Ghulam Muhammad +6 more
TL;DR: An Arabic voice pathology database (AVPD) is designed and developed in this study by recording three vowels, running speech, and isolated words and the shortcomings of different voice disorder databases were identified so that they could be avoided in the AVPD.
Journal ArticleDOI
Voice Pathology Detection and Classification Using Auto-Correlation and Entropy Features in Different Frequency Regions
Ahmed Al-nasheri,Ghulam Muhammad,Mansour Alsulaiman,Zulfiqar Ali,Khalid H. Malki,Tamer A. Mesallam,Mohamed F. Ibrahim +6 more
TL;DR: This paper concentrates on developing an accurate and robust feature extraction for detecting and classifying voice pathologies by investigating different frequency bands using autocorrelation and entropy using a support vector machine as a classifier.
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An Investigation of Multidimensional Voice Program Parameters in Three Different Databases for Voice Pathology Detection and Classification.
Ahmed Al-nasheri,Ghulam Muhammad,Mansour Alsulaiman,Zulfiqar Ali,Tamer A. Mesallam,Mohamed Farahat,Khalid H. Malki,Mohamed A. Bencherif +7 more
TL;DR: Investigation of Multidimensional Voice Program parameters to automatically detect and classify the voice pathologies in multiple databases finds a clear difference in the performance of the MDVP parameters using these databases.
Journal ArticleDOI
Voice pathology detection using interlaced derivative pattern on glottal source excitation
Ghulam Muhammad,Mansour Alsulaiman,Zulfiqar Ali,Zulfiqar Ali,Tamer A. Mesallam,Mohamed Farahat,Khalid H. Malki,Ahmed Al-nasheri,Mohamed A. Bencherif +8 more
TL;DR: Experimental results show that the IDP based features give higher accuracy than that using other related features in all the three databases, and the accuracies using cross-databases are also high using theIDP features.
Journal ArticleDOI
Investigation of Voice Pathology Detection and Classification on Different Frequency Regions Using Correlation Functions
TL;DR: 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 by extracting maximum peak values and their corresponding lag values from each frame of a voiced signal.