New time-scale warping algorithm and associated computational considerations for single dimensional and multidemensional speech parameter contours
A. Maheswaran, RE Bonger · 1st IASTED International Symposium on Signal Processing and its Applications · 1987
In this paper a new sample association approach to be known as the Hilbert Warping (HW) algorithm and associated computational considerations are described. The HW algorithm is chosen from the observation that signals of similar form but with different time scales appear as similar trajectories when represented by suitable two dimensional plots in the X-Y plane, and it overcomes difficulties such as Identification of signal endpoints and assumptions about the smooth nature of warping that are permissible, associated with dynamic programming algorithms. The HW algorithm can be applied to both single dimensional and multi-dimensional signals as in dynamic programming algorithms. The paper also shows computational savings are achieved with no loss of recognition accuracy by the HW algorithm when compared to DTW algorithms.