A DOA Based Speaker Diarization System for Real Meetings

Abstract

This paper presents a speaker diarization system that estimates who spoke when in a meeting. Our proposed system is realized by using a noise robust voice activity detector (VAD), a direction of arrival (DOA) estimator, and a DOA classifier. Our previous system utilized the generalized cross correlation method with the phase transform (GCC-PHAT) approach for the DOA estimation. Because the GCC-PHAT can estimate just one DOA per frame, it was difficult to handle speaker overlaps. This paper tries to deal with this issue by employing a DOA at each time-frequency slot (TFDOA), and reports how it improves diarization performance for real meetings / conversations recorded in a room with a reverberation time of 350 ms.