In this paper we describe the ICSI-SRI entry in the Rich Transcription 2005 Spring Meeting Recognition Evaluation. The current system is based on the ICSI-SRI clustering system for Broadcast News (BN), with extra modules to process the different meetings tasks in which we participated. Our base system uses agglomerative clustering with a modified Bayesian Information Criterion (BIC) measure to determine when to stop merging clusters and to decide which pairs of clusters to merge. This approach does not require any pre-trained models, thus increasing robustness and simplifying the port from BN to the meetings domain. For the meetings domain, we have added several features to our baseline clustering system, including a “purification” module that tries to keep the clusters acoustically homogeneous throughout the clustering process, and a delay&sum beamforming algorithm which enhances signal quality for the multiple distant microphones (MDM) sub-task. In post-evaluation work we further improved the delay&sum algorithm, experimented with a new speech/non-speech detector and proposed a new system for the lecture room environment.
['Xavier Anguera', 'Chuck Wooters', 'Barbara Peskin', 'Mateu Aguilo']
Robust speaker segmentation for meetings: the ICSI-SRI spring 2005 diarization system