Dynamic speech parameterization for text-independent phone segmentation

In this work, a dynamic speech parameterization based on the continuous multiresolution divergence is used to modify a text-independent phone segmentation algorithm. This encoding is employed as input and also replaces an stage of the segmentation procedure responsible for the estimation of the intensity of changes in signal features. The segmentation performance of this representation has been compared with the original algorithm using as input a classical Melbank parameterization and speech representation based on the continuous multiresolution divergence. The results indicate that the modification here proposed increases the ability of the algorithm to perform the segmentation task. This suggests that continuous multiresolution divergence provides valuable information related to acoustic features that take into account phoneme transitions. Moreover, this parameterization gives enough information for its direct use without further processing.