Open-set speaker identification with diverse-duration speech data

Abstract

Rawande Karadaghi, Heinz Hertlein, and, Aladdin Ariyaeeinia, "Open-set speaker identification with diverse-duration speech data", in Proceedings of SPIE 9457, Biometric and Surveillance Technology for Human Activity Identification XII, Baltimore, USA, 20 April 2015. DOI:10.1117/12.2176335?? 2015 SPIE.The concern in this paper is an important category of applications of open-set speaker identification in criminal investigation, which involves operating with short and varied duration speech. The study presents investigations into the adverse effects of such an operating condition on the accuracy of open-set speaker identification, based on both GMMUBM and i-vector approaches. The experiments are conducted using a protocol developed for the identification task, based on the NIST speaker recognition evaluation corpus of 2008. In order to closely cover the real-world operating conditions in the considered application area, the study includes experiments with various combinations of training and testing data duration. The paper details the characteristics of the experimental investigations conducted and provides a thorough analysis of the results obtained