Full-Posterior PLDA based Speaker Diarization of telephone conversations

Conventional speaker diarization systems based on factor analysis mainly differ in i-vector scoring, such as the cosine scoring and the newly emerged probabilistic linear discriminant analysis (PLDA) scoring technique. However, during the clustering process, the accuracy of PLDA scoring decreases in short speech segments. The matter becomes even worse when the segments are with arbitrary duration. In this paper, we choose a modified PLDA model, called full posterior distribution PLDA (FP-PLDA) for clustering instead of the standard PLDA model (Std-PLDA). The new model exploits the intrinsic uncertainty of the i-vector extraction. The experiment shows that FP-PLDA has an especially effective performance in the short and variable duration speech segments. It relatively decreases the diarization error rate by around 41% for the cosine scoring system and 30.98% for the standard PLDA system.