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.