Robust Feature Extraction from AD-HOC Microphones for Meeting Diarization

Speaker diarization in the meeting scenario is a challenging problem due to spontaneous, conversational speech and distant nature of signal acquisition. We present a novel method that operates with the distant microphones placed in unknown locations and with ad-hoc configurations in a room. The method exploits robust spatial features and combines them with noise robust spectral features, enhanced using a modulation domain algorithm. The spatial features extracted include a model of the time-difference-of-arrival (TDOA) information along with optimal channel-pair selection, based on which enhanced spectral features are computed. We present results on the NIST RT05 meeting corpora and show that the proposed method significantly reduces the diarization error rate compared to a baseline diarization system, outperforming a previous method by a relative 33.1 % DER.