Forensic Identification Reporting Using A GMM Based Speaker Recognition System Dedicated to Algerian Arabic Dialect Speakers

Abstract

Starting from the fact of the lack of Arabic databases dedicated to performance evaluation of speaker recognition and forensic reporting systems. We present in this paper our experience in constructing an Algerian dialect database and the motivation beyond this work. After that, the corpus based Bayesian framework for interpretation of evidence in forensic systems in terms of likelihood ratio (LR) is applied on this database under different recording conditions: microphone, fixed and cellular. Preliminary results in terms of Receiver Operating Characteristics (ROC) and TIPPET plots show higher accuracy in matched conditions of training and testing. However, the performance degrades significantly in mismatched conditions.