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Stream-Based Classification and Segmentation of Speech Events in Meeting Recordings

  • Jun Ogata
  • Futoshi Asano
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4105)

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.

Keywords

Hide Markov Model Bayesian Information Criterion Speech Recognition Automatic Speech Recognition Acoustic Event 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Ogata
    • 1
  • Futoshi Asano
    • 1
  1. 1.National Institute of Advanced Industrial Science and Technology (AIST)IbarakiJapan

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