Some techniques for incorporating local timescale variability information into a dynamic time-warping algorithm for automatic speech recognition

Dynamic Time-Warping is one the most important tools available for overcoming timescale variability problems in Automatic Speech Recognition. One of the main problems associated with the technique is to constrain the behaviour of the algorithm in order to avoid unlikely timescale distortion. This paper describes techniques for incorporating information about timescale variability directly into the Dynamic Time-Warping process. Results are presented which show that these techniques can lead to considerable improvements in recognition accuracy, especially if the differences between word classes are mainly due to temporal structure.