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Dynamic time warping (DTW) can be used to compute the similarity between twosequences of generally differing length. We propose a modification to DTW thatperforms individual and independent pairwise alignment of feature trajectories.The modified technique, termed feature trajectory dynamic time warping (FTDTW),is applied as a similarity measure in the agglomerative hierarchical clusteringof speech segments. Experiments using MFCC and PLP parametrisations extractedfrom TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent andstatistically significant improvements in the quality of the resulting clustersin terms of F-measure and normalised mutual information (NMI).

Feature Trajectory Dynamic Time Warping for Clustering of Speech Segments