In this paper, we proposed an algorithm used to improve the performance of the metric-based segmentation techniques, by which the segmentation points are found at maxima of a distance measured between two contiguous windows shifted along the stream of speech features. In our proposed method, the PCA processes are first performed on the speech features to obtain more robust features, and then the above metric-based segmentation was applied on the PCA-derived features to decide the segmentation points. Experiment results show that our proposed method can efficiently improve the detection rates of the segmentation points up to 7% while the false alarm rates remain unchanged.
Cite as: Hung, J.-W., Wang, H.-M., Lee, L.-S. (2000) Automatic metric-based speech segmentation for broadcast news via principal component analysis. Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000), vol. 4, 121-124, doi: 10.21437/ICSLP.2000-766
@inproceedings{hung00_icslp,
author={Jeih-Weih Hung and Hsin-Min Wang and Lin-Shan Lee},
title={{Automatic metric-based speech segmentation for broadcast news via principal component analysis}},
year=2000,
booktitle={Proc. 6th International Conference on Spoken Language Processing (ICSLP 2000)},
pages={vol. 4, 121-124},
doi={10.21437/ICSLP.2000-766}
}