Abstract
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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Anguera, X., Wooters, C., Peskin, B., Aguiló, M. (2006). Robust Speaker Segmentation for Meetings: The ICSI-SRI Spring 2005 Diarization System. In: Renals, S., Bengio, S. (eds) Machine Learning for Multimodal Interaction. MLMI 2005. Lecture Notes in Computer Science, vol 3869. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11677482_34
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DOI: https://doi.org/10.1007/11677482_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-32549-9
Online ISBN: 978-3-540-32550-5
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