A new time-scale warping algorithm and associated modules for single dimensional and multidimensional speech parameter contours

In this paper a new sample association approach to be known as the Hilbert Warping (HW) algorithm and associated modules are described. This 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 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.