8th European Conference on Speech Communication and Technology

Geneva, Switzerland
September 1-4, 2003


A Sequential Metric-Based Audio Segmentation Method via the Bayesian Information Criterion

Shi-sian Cheng, Hsin-Min Wang

Academia Sinica, Taiwan

In this paper, we propose a sequential metric-based audio segmentation method that has the advantage of low computation cost of metric-based methods and the advantage of high accuracy of model-selection-based methods. There are two major differences between our method and the conventional metric-based methods:(1) Each changing point has multiple chances to be detected by different pairs of windows, rather than only once by its neighboring acoustic information.(2) By introducing the Bayesian Information Criterion(BIC) into the distance computation of two windows, we can deal with the thresholding issue more easily. We used five one-hour broadcast news shows for experiments, and the experimental results show that our method performs as well as the model-selection-based methods, but with a lower computation cost.

Full Paper

Bibliographic reference.  Cheng, Shi-sian / Wang, Hsin-Min (2003): "A sequential metric-based audio segmentation method via the Bayesian information criterion", In EUROSPEECH-2003, 945-948.