Speech segmentation using divergence algorithm with Zero Crossing property

Abstract

Divergence algorithm is a statistical segmentation approach which finds segmentation point via detection of abrupt changes without any previous information of the acoustic signal. The approach could get high match of segmentation but also gives a lot of false segmentation points. This work introduced a property based on the usage of Zero Crossing Rate (ZCR) in enhancing segmentation by divergence algorithm. The work starts via optimizing divergence algorithm segmentation performance via parameters tuning. Then the proposed property based on ZCR is applied to divergence algorithm to reduce insertion points. The results of tuning divergence parameters achieved match rate of 99.4% at time tolerance of 0.09 seconds with 69% insertion rate occurrences in comparisons to reference points. The result in applying the introduced ZCR property to divergence algorithm shows that tuning of some ZCR property parameters could reduce insertion between 4% to 45%. However, it would also reduce the match rate. Nevertheless, the method could reduced insertion rate by 5.5% while maintaining match rate of 99.4%.