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.