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Mobile Information Systems
Volume 2017, Article ID 6986391, 6 pages
Research Article

Automatic Speaker Recognition for Mobile Forensic Applications

Center of Smart Robotics Research (CS2R), College of Computer and Information Sciences (CCIS), King Saud University (KSU), Riyadh, Saudi Arabia

Correspondence should be addressed to Mohammed Algabri; as.ude.usk@irbaglam

Received 2 November 2016; Accepted 10 January 2017; Published 13 March 2017

Academic Editor: Eugenijus Kurilovas

Copyright © 2017 Mohammed Algabri et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Presently, lawyers, law enforcement agencies, and judges in courts use speech and other biometric features to recognize suspects. In general, speaker recognition is used for discriminating people based on their voices. The process of determining, if a suspected speaker is the source of trace, is called forensic speaker recognition. In such applications, the voice samples are most probably noisy, the recording sessions might mismatch each other, the sessions might not contain sufficient recording for recognition purposes, and the suspect voices are recorded through mobile channel. The identification of a person through his voice within a forensic quality context is challenging. In this paper, we propose a method for forensic speaker recognition for the Arabic language; the King Saud University Arabic Speech Database is used for obtaining experimental results. The advantage of this database is that each speaker’s voice is recorded in both clean and noisy environments, through a microphone and a mobile channel. This diversity facilitates its usage in forensic experimentations. Mel-Frequency Cepstral Coefficients are used for feature extraction and the Gaussian mixture model-universal background model is used for speaker modeling. Our approach has shown low equal error rates (EER), within noisy environments and with very short test samples.