Using acoustic condition clustering to improve acoustic change detection on broadcast news

We have developed a system that breaks input speech into segments using an acoustic similarity measure. The aim is to detect the time points where the acoustic characteristics change, usually due to speaker changes but also resulting from changes in the acoustic environment. We have also developed a system to cluster the segments generated by the first system into clusters composed of homogeneous acoustic conditions. In this paper, we present a technique to improve the robustness of the acoustic change detection by feeding back the results of the segment clustering, exploiting the extra information available in the distance between the two clusters to which the segments belong. The interaction between the acoustic change detection and clustering systems gives us a substantial improvement over results previously reported on the 1997 Hub-4 Broadcast News test set that we employed [1][2]: Feedback of clustering information improved the Equal Error Rate (EER) of our acoustic change detection (ACD) system from 26.5% to 18%.