Variational Bayes based I-vector for speaker diarization of telephone conversations

In this paper, we investigate the variational Bayes based I-vector method for speaker diarization of telephone conversations. The motivation of the proposed algorithm is to utilize variational Bayesian framework and exploit potential channel effect of total variability modeling for diarization of conversation side. Other three well-known techniques are compared as follows: K-means clustering for eigenvoices and I-vector speaker diarization, and variational Bayes applied to eigenvoices. Performance evaluations are conducted on the summed-channel telephone data from the 2008 NIST speaker recognition evaluation. The paper discusses how the performance is influenced by different modules, e.g., VAD, initial speaker clustering and Viterbi re-segmentation. Comparison experiments show the interest of variational Bayesian probabilistic framework for speaker diarization.