Maximum-Likelihood Online Speaker Diarization in Noisy Meetings Based on Categorical Mixture Model and Probabilistic Spatial Dictionary

Abstract

In this paper, we propose a maximum-likelihood online diarization method based on a probabilistic spatial dictionary. This dictionary consists of the given probability distribution of spatial features for each possible direction of arrival (DOA) of source signals. Recently, we have developed an online, noise-robust diarization method by utilizing this dictionary as spatial prior information. In this method, DOA estimation is first performed frame-wise based on the dictionary, and subsequently diarization is performed. Although the DOA estimation is performed optimally in the maximum-likelihood sense, the diarization is performed suboptimally based on some heuristics. In contrast, the proposed method performs DOA estimation and diarization jointly and optimally in the maximum-likelihood sense. This is realized by introducing a categorical mixture model (CMM), which has source-wise DOA information and diarization information as unknown parameters. We conducted an experiment on a real-world meeting dataset, and confirmed that the proposed method reduced a diarization error rate by absolute 2.7% compared to the above conventional method.