TOWARDS ROBUST SPEAKER SEGMENTATION: THE ICSI-SRI FALL 2004 DIARIZATION SYSTEM

Abstract

We describe the ICSI-SRI entry in the Fall 2004 DARPA EARS Metadata Evaluation. The current system was derived from ICSI’s Fall 2003 Speaker-attributed STT system. Our system is an agglomerative clustering system that uses a BIC-like measure to determine when to stop merging clusters and to decide which pairs of clusters to merge. The main advantage of this approach is that it does not require pre-trained acoustic models, providing robustness and portability. Changes for this year’s system include: different front-end features, the addition of SRI’s Broadcast News speech/non-speech detector, and modifications to the segmentation routine. In post-evaluation work, we found further improvement by changing the stopping criterion from the BIC-like measure to a Viterbi measure. Additionally, we have explored issues related to pruning and improved initialization. 1.