New Methods for Template Selection and Compression in Continuous Speech Recognition

摘要

We propose a maximum likelihood method for selecting template representatives, and in order to include more information in the selected template representatives, we further propose to create compressed template representatives by Gaussian mixture model (GMM) merging algorithm. A Kullback-Leibler (KL) divergence based local distance is proposed for Dynamic Time Warping (DTW) in template matching. Experimental results on the tasks of TIMIT phone recognition and large vocabulary continuous speech recognition demonstrated that the proposed template selection method significantly improved the recognition accuracy over the HMM baseline while only 5% or 10% templates were selected from the total templates, and the template compression method has provided further recognition accuracy gains over the template selection method.