Dynamic Time Warping (DTW), a pattern matching technique traditionally used for restricted vocabulary speech recognition, is based on a temporal alignment of the input signal with the template models. The principal drawback of DTW is its high computational cost as the lengths of the signals increase. This paper shows extended results over our previously published conference paper, which introduces an optimized version of the DTW that is based on the Discrete Wavelet Transform (DWT). © 2008 Elsevier B.V. All rights reserved.
Barbon, S., Guido, R. C., Vieira, L. S., Silva Fonseca, E., Sanchez, F. L., Scalassara, P. R., … Chen, S. H. (2009). Wavelet-based dynamic time warping. Journal of Computational and Applied Mathematics, 227(2), 271–287. https://doi.org/10.1016/j.cam.2008.03.015