Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Automatic Metric-Based Speech Segmentation for Broadcast News via Principal Component Analysis

Jeih-Weih Hung (1,2), Hsin-Min Wang (1), Lin-Shan Lee (1,2)

(1) Institute of Information Science, Academia Sinica;
(2) Dept of Electrical Engineering, Taiwan University; Taipei, Taiwan

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.

Full Paper

Bibliographic reference.  Hung, Jeih-Weih / Wang, Hsin-Min / Lee, Lin-Shan (2000): "Automatic metric-based speech segmentation for broadcast news via principal component analysis", In ICSLP-2000, vol.4, 121-124.