Robust speaker diarization for meetings: ICSI RT06s evaluation system
Xavier Anguera, Chuck Wooters, José Manuel Pardo · 2006
Abstract. In this paper we present the ICSI speaker diarization system submitted for the NIST Rich Transcription evaluation (RT06s) [1] con-ducted on the meetings environment. The presented system is based on the RT05s system, which uses agglomerative clustering with a modified Bayesian Information Criterion (BIC) measure to decide which pairs of clusters to merge and to determine when to stop merging clusters. In this year’s system we have eliminated any remaining need for training data, therefore increasing robustness. In our primary system we have intro-duced several improvements from last year. First, we use a new training-free speech/non-speech detection algorithm. Second, we introduce a new algorithm for system initialization. The third improvement is the use of a frame purification algorithm to increase cluster discriminability. Fi-nally, we describe the use of inter-channel delays as features. We explain each of these improvements and show our system’s results on the official evaluation data using hand-aligned references and forced-alignments. We also analyze some of the results and propose improvements. 1