Speaker diarization of meetings based on large TDOA feature vectors

This paper investigates the use of large TDOA feature vectors together with acoustic information in speaker diarization of meetings. TDOAs are obtained by considering all possible microphones pairs and this approach is compared with conventional TDOA features extracted w.r.t. a reference channel. The study is carried using two systems, the first based on Gaussian Mixture Modeling and the second based on the Information Bottleneck approach. Results on NIST RT06/RT07/RT09 evaluation datasets show a large speaker error reduction of 30% relative going from 14.3% to 10.8% for the first and from 12.3% to 8.2% for the second whenever the feature weighting is properly handled. Furthermore results reveal that the IB system is more robust to different number of microphones even when all pairs large TDOA vectors are used thus outperforming the HMM/GMM by 25% relative (8.2% error compared to 10.8%).