This paper presents two approaches to speaker clustering based on Probabilistic Linear Discriminant Analysis (PLDA) in the speaker diarization task. We refer to the approaches as the multifold-PLDA approach and the onefold-PLDA approach. For both approaches, simple factor analysis model 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 model. Further, two-stage clustering with Bayesian Information Criterion (BIC) based approach applied in the first stage and the PLDA-based approach in the second stage is examined. We carried out our experiments using the COST278 multilingual broadcast news database. The best evaluated system yielded 42% relative improvement of the speaker error rate over a baseline BIC-based system.
Bibliographic reference. Silovsky, Jan / Prazak, Jan / Cerva, Petr / Zdansky, Jindrich / Nouza, Jan (2011): "PLDA-based clustering for speaker diarization of broadcast streams", In INTERSPEECH-2011, 2909-2912.