Improved Dynamic Time Warping Based on the Discrete Wavelet Transform

Dynamic Time Warping (DTW) is a pattern matching approach that can be used for limited vocabulary speech recognition, which is based on a temporal alignment of the input signal with the template models. The main drawback of this method is its high computational cost when the length of the signals increases. This paper presents a modified ver- sion of the DTW, based on the Discrete Wavelet Transform (DWT), that reduces its original complexity. Many wavelet families with different support-sizes are experimented and the corresponding results are reported.