Eigenchannel Compensation and Symmetric Score for Robust Text-Independent Speaker Verification

Abstract

The negative effect of the session variability has become more and more severe for the performance of the speaker verification system. This paper discusses the eigenchannel compensation and investigates the symmetric scoring method to diminish the session variability and further enhance the performance. Experiments were conducted on the core tests of the 2006 and 2008 speaker recognition evaluation (SRE) corpuses of the national institute of standards and technology (NIST) respectively. The experimental results demonstrate that the eigenchannel compensation can achieve excellent improvement and the symmetric scoring, as a measurement of cross similarity, can further improve the performance moderately. Overall, the system performance can be significantly improved, with equal error rate from 9.74% to 5.08% , 47.8% on SRE06 corpus and from 16.26% to 9.42% , 42.1% on SRE08 corpus while detection cost function from 0.0456 to 0.0263 , 42.3% on SRE06 corpus and from 0.0692 to 0.0449 , 35.1% on SRE08 corpus.