Feature trajectory dynamic time warping for clustering of speech segments
CITATION: Lerato, L. & Niesle, T. 2019. Feature trajectory dynamic time warping for clustering of speech segments. EURASIP Journal on Audio, Speech, and Music Processing, 2019:6, doi:10.1186/s13636-019-0149-9.
The original publication is available at https://asmp-eurasipjournals.springeropen.com
ENGLISH ABSTRACT: Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differinglength. We propose a modification to DTW that performs individual and independent pairwise alignment of featuretrajectories. The modified technique, termed feature trajectory dynamic time warping (FTDTW), is applied as asimilarity measure in the agglomerative hierarchical clustering of speech segments. Experiments using MFCC and PLPparametrisations extracted from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent andstatistically significant improvements in the quality of the resulting clusters in terms of F-measure and normalisedmutual information (NMI).