SVM decision taking in I-Vector based Speaker Verification Systems



State of the art speaker verification systems provide a decision based on cosine distance. A different approach is to consider a decision based on Support Vector Machine (SVM) separation. SVMs have been largely used in pattern recognition and decision making as well as in combination with GMM super-vectors (GSV). However, the idea of using SVMs in the i-vector space for speaker recognition has not been well explored. We will study the different kernel functions and how it could be used in speaker verification with i-vector space. We will explore how we could deal with channel and speaker variabilities in the feature space. The experiments are done on NIST-SRE 2010 core condition database.