The research of speaker diarization based on BIC and G_PLDA

The traditional technology for speaker diarization(SD), which exploits the Bayesian information criterion(BIC) as the similarity metric, can obtain good results in the short dialogue task, but with the length of the dialogue increasing , single Gaussian model of BIC is insufficient to describe the information distribution of different speakers. Moveover, it is difficult to delineate the threshold between the same speakers and different speakers when using hierarchical clustering (HAC). To solve this problem, a fusion method between BIC and G_PLDA was proposed, so as to make full use of the reliability of BIC in short-term clustering and the excellent discriminating power of G_PLDA in long utterancs. A set of experiments based on NIST 08 Summed shows that this new fusion method reduces the diariazation error rate (DER) from 2.34% of BIC baseline system to 1.54%, improving performance of speaker diarization by 34.2%.