The Segmental Bayesian Information Criterion and Its Applications to Speaker Diarization

This paper discusses the use of the BIC with respect to speaker diarization, i.e., the problem of assigning the observation vectors of an audio file to a set of speakers of unknown cardinality. Our primary goals are to examine the two dominant approaches of the BIC, namely the global and the local and combine the strengths of the two variants into one intuitive criterion, the segmental-BIC. We then consider the asymptotic behavior of the segmental-BIC, when dealing with models that are highly misspecified, as the ones commonly used in the speaker diarization task. Our main result is a modified version of the BIC, which significantly outperforms the current variants over the entire range of operating points, and achieves performance close to those of highly computationally demanding algorithms.