Speaker diarization using PLDA-based speaker clustering

This paper investigates application of the Probabilistic Linear Discriminant Analysis (PLDA) for speaker clustering within a speaker diarization framework. Factor analysis is employed to extract low-dimensional representation of a sequence of acoustic feature vectors - so called i-vectors - and these i-vectors are modeled using the PLDA. Experiments were carried out using the COST278 broadcast news database. We achieved 33.7% relative improvement of the Diarization Error Rate (DER) and 43.8% relative improvement of the speaker error rate compared to the baseline system using clustering based on the Bayesian Information Criterion (BIC).