Speaker indexing and speech enhancement in real meetings / conversations

Abstract

This paper presents a speaker indexing method that uses a small number of microphones to estimate who spoke when. Our proposed speaker indexing is realized by using a noise robust voice activity detector (VAD), a QCC-PHAT based direction of arrival (DOA) estimator, and a DOA classifier. Using the estimated speaker indexing information, we can also enhance the utterances of each speaker with a maximum signal-to-noise-ratio (MaxSNR) beamformer. This paper applies our system to real recorded meetings / conversations recorded in a room with a reverberation time of 350 ms, and evaluates the performance by a standard measure: the diarization error rate (DER). Even for the real conversations, which have many speaker turn-takings and overlaps, the speaker error time was very small with our proposed system. We are planning to demonstrate a real-time speaker indexing system at ICASSP2008.