Ravichander Vipperla, Juergen T Geiger, Simon Bozonnet, Dong Wang, Nicholas Evans, Bjorn Schuller and Gerhard Rigoll
ICASSP 2012, 37th International Conference on Acoustics, Speech and Signal Processing, March 25-30, 2012, Kyoto, Japan
Abstract: Overlapping speech is known to degrade speaker diarization performance with impacts on speaker clustering and segmentation. While previous work made important advances in detecting overlapping speech intervals and in attributing them to relevant speakers, the problem remains largely unsolved. This paper reports the first application of convolutive non-negative sparse coding (CNSC) to the overlap problem. CNSC aims to decompose a composite signal into its underlying contributory parts and is thus naturally suited to overlap detection and attribution. Experimental results on NIST RT data show that the CNSC approach gives comparable results to a state-of-the-art hidden Markov model based overlap detector. In a practical diarization system, CNSC based speaker attribution is shown to reduce the speaker error by over 40% relative in overlapping segments.