Title
New Methods For Template Selection And Compression In Continuous Speech Recognition
Abstract
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.
Year
Venue
Keywords
2011
12TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION 2011 (INTERSPEECH 2011), VOLS 1-5
template matching, template selection, KL divergence, DTW
Field
DocType
Citations 
Pattern recognition,Computer science,Speech recognition,Artificial intelligence
Conference
4
PageRank 
References 
Authors
0.41
1
2
Name
Order
Citations
PageRank
Xie Sun1121.96
Yunxin Zhao2807121.74