DOAJ Open Access 2019

Feature trajectory dynamic time warping for clustering of speech segments

Lerato Lerato Thomas Niesler

Abstrak

Abstract Dynamic time warping (DTW) can be used to compute the similarity between two sequences of generally differing length. We propose a modification to DTW that performs 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 clustering of speech segments. Experiments using MFCC and PLP parametrisations extracted from TIMIT and from the Spoken Arabic Digit Dataset (SADD) show consistent and statistically significant improvements in the quality of the resulting clusters in terms of F-measure and normalised mutual information (NMI).

Topik & Kata Kunci

Acoustics. Sound Electronic computers. Computer science

Penulis (2)

Lerato Lerato

Thomas Niesler

Format Sitasi

APA MLA BibTeX

Lerato, L., Niesler, T. (2019). Feature trajectory dynamic time warping for clustering of speech segments. https://doi.org/10.1186/s13636-019-0149-9

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Informasi Jurnal

Tahun Terbit: 2019
Sumber Database: DOAJ
DOI: 10.1186/s13636-019-0149-9
Akses: Open Access ✓