Maximum Likelihood i-vector Space Using PCA for Speaker Verification

摘要

This paper proposes a new approach to training the i-vector space using a variant of PCA with the Baum-Welch statistics for speaker verification. In eigenvoice the rank of variability space is bounded by the number of training speakers, so a variant of the probabilistic PCA approach is introduced for estimating the parameters. But this constraint doesn't exist in i-vector model because the number of utterances is much bigger than the rank of total variability space. We adopt the EM algorithm for PCA with the statistics to train the total variability space, and the maximum likelihood criterion is used. After WCCN, the cosine similarity scoring is used for decision. These two total variability spaces will be fused at feature-level and score-level. The experiments have been run on the NIST SRE 2008 data, and the results show that the performances in two total variability spaces are comparable. The performance can be improved obviously after feature fusion and score fusion.