Feature trajectory dynamic time warping for clustering of speech segments
Date
2019
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
SpringerOpen
Abstract
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).
Description
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
The original publication is available at https://asmp-eurasipjournals.springeropen.com
Keywords
Speech segments, Agglomerations, Industrial, Hierarchical clustering (Cluster analysis)
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