Improved speaker diarization of meeting speech with recurrent selection of representative speech segments and participant interaction pattern modeling

In this work we describe two distinct novel improvements to our speaker diarization system, previously proposed for analysis of meeting speech. The first approach focuses on recurrent selection of representative speech segments for speaker clustering while the other is based on participant interaction pattern modeling. The former selects speech segments with high relevance to speaker clustering, especially from a robust cluster modeling perspective, and keeps updating them throughout clustering procedures. The latter statistically models conversation patterns between meeting participants and applies it as a priori information when refining diarization results. Experimental results reveal that the two proposed approaches provide performance enhancement by 29.82% (relative) in terms of diarization error rate in tests on 13 meeting excerpts from various meeting speech corpora.