Introduction - If you have any usage issues, please Google them yourself
Searching for the best path that matches two time-series signals is the main task for many researchers, because of its importance in these applications. Dynamic Time-Warping (DTW) is one of the prominent techniques to accomplish this task, especially in speech recognition systems. DTW is a cost minimisation matching technique, in which a test signal is stretched or compressed according to a reference template.
Although there are other advanced techniques in speech recognition such as the hidden Markov modelling (HMM) and artificial neural network (ANN) techniques, the DTW is widely used in the small-scale embedded-speech recognition systems such as those embedded in cell phones. The reason for this is owing to the simplicity of the hardware implementation of the DTW engine, which makes it suitable for many mobile devices. Additionally, the training procedure in DTW is very simple and fast, as compared with the HMM and ANN rivals.