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Mathematical Problems in Engineering
Volume 2014, Article ID 898729, 8 pages
Research Article

An HMM-Like Dynamic Time Warping Scheme for Automatic Speech Recognition

Department of Electrical Engineering, National Formosa University, No. 64, Wunhua Road, Huwei Township, Yunlin County 632, Taiwan

Received 30 March 2014; Accepted 18 May 2014; Published 8 July 2014

Academic Editor: Teen-Hang Meen

Copyright © 2014 Ing-Jr Ding and Yen-Ming Hsu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In the past, the kernel of automatic speech recognition (ASR) is dynamic time warping (DTW), which is feature-based template matching and belongs to the category technique of dynamic programming (DP). Although DTW is an early developed ASR technique, DTW has been popular in lots of applications. DTW is playing an important role for the known Kinect-based gesture recognition application now. This paper proposed an intelligent speech recognition system using an improved DTW approach for multimedia and home automation services. The improved DTW presented in this work, called HMM-like DTW, is essentially a hidden Markov model- (HMM-) like method where the concept of the typical HMM statistical model is brought into the design of DTW. The developed HMM-like DTW method, transforming feature-based DTW recognition into model-based DTW recognition, will be able to behave as the HMM recognition technique and therefore proposed HMM-like DTW with the HMM-like recognition model will have the capability to further perform model adaptation (also known as speaker adaptation). A series of experimental results in home automation-based multimedia access service environments demonstrated the superiority and effectiveness of the developed smart speech recognition system by HMM-like DTW.