Entropy-based variable frame rate analysis of speech signals and its application to ASR

Most speech processing algorithms analyze speech signals frame by frame with a fixed frame rate. Fixed-rate analysis is inconsistent with human speech perception and effectively assigns the same importance or ´weight´ to all equi-duration frames. In Zhu et al. (2000), we proposed a variable frame rate (VFR) analysis technique that is based on a Euclidian distance measure. In this paper, we propose another approach for VFR based on the entropy of the signal. We compare entropy and Euclidian distance measures for VFR in ASR experiments using the Aurora2 and T146 databases. Better performance is observed for the entropy-based VFR over our earlier VFR approach and over the fixed-rate system.