Stream-based classification and segmentation of speech events in meeting recordings

Authors:
Jun Ogata;Futoshi Asano
Affiliations:
National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan;National Institute of Advanced Industrial Science and Technology (AIST), Ibaraki, Japan
Venue:
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Year:
2006

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Abstract

In this paper, we presents a stream-based speech event classification and segmentation method in meeting recordings. Four speech events are considered: normal speech, laughter, cough and pause between talks. hidden Markov Models (HMMs) are used to model these speech events and a model topology optimization using Bayesian Information Criterion (BIC) is applied. Experimental results have shown that our system can obtain satisfying results. Based on the detected speech events, the recording of the meeting is structured using an XML-based description language and is visualized by a browser.