Novel strategies for reducing the false alarm rate in a speaker segmentation system

Reliable speaker segmentation is critical in many applications in the speech processing domain. In this paper, we extend our earlier formulation for false alarm reduction in a typical state-of-art speaker segmentation system. Specifically, we present two novel strategies for reducing the false alarm rate with a minimal impact on the true speaker change detection rate. One of the new strategies rejects, given a discard probability, those changes that are suspicious of being false alarms because of their low ΔBIC value; and the other one assumes that the occurrence of changes constitute a Poisson process, so changes will be discarded with a probability that follows a Poisson cumulative density function. Our experiments show the improvements obtained with each false alarm reduction approach using the Spanish Parliament Sessions defined for the 2006 TC-STAR Automatic Speech Recognition evaluation campaign.