Partial sequence matching using an Unbounded Dynamic Time Warping algorithm

Before the advent of Hidden Markov Models(HMM)-based speech recognition, many speech applications were built using pattern matching algorithms like the Dynamic Time Warping (DTW) algorithm, which are generally robust to noise and easy to implement. The standard DTW algorithm usually suffers from lack of flexibility on start-end matching points and has high computational costs. Although some DTW-based algorithms have been proposed over the years to solve either one of these problems, none is able to discover multiple alignment paths with low computational costs. In this paper, we present an “unbounded” version on the DTW (U-DTW in short) that is computationally lightweight and allows for total flexibility on where the matching segment occurs. Results on a word matching database show very competitive performances both in accuracy and processing time compared to existing alternatives.