Svm-Based Speaker Verification by Location in the Space of Reference Speakers

In this paper, we investigate SVM-based speaker verification by location in the space of reference speakers. Speaker location is represented by a vector of log-likelihoods of utterance data given reference speaker models. Channel or session variability in speaker locations due to microphone, acoustic environments etc. would impair verification performance. To reduce such variability, within-class covariance normalization (WCCN), nuisance attribute projection (NAP) and their combination are applied, and significant performance improvements are obtained. Experimental results on a NIST SRE 2006 task show that this location SVM system achieves comparable performance to a state-of-art cepstral GMM-UBM verification system, and their fusion can give additional performance gains.