Priors for Speaker Counting and Diarization with AHC

Estimating the number of speakers in an audio segment is a necessary step in the process of speaker diarization, but current diarization algorithms do not explicitly define a prior probability on this estimation. This work proposes a process for including priors in speaker diarization with agglomerative hierarchical clustering (AHC). It is also shown that the exclusion of a prior with AHC is itself implicitly a prior, which is found to be geometric growth in the number of speakers. By using more sensible priors, we are able to demonstrate significantly improved robustness to calibration error for speaker counting and speaker diarization.