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Mathematical Problems in Engineering
Volume 2013, Article ID 542680, 10 pages
http://dx.doi.org/10.1155/2013/542680
Research Article

Developments of Machine Learning Schemes for Dynamic Time-Wrapping-Based Speech Recognition

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

Received 13 September 2013; Accepted 22 October 2013

Academic Editor: Teen-Hang Meen

Copyright © 2013 Ing-Jr Ding et al. 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.

Abstract

This paper presents a machine learning scheme for dynamic time-wrapping-based (DTW) speech recognition. Two categories of learning strategies, supervised and unsupervised, were developed for DTW. Two supervised learning methods, incremental learning and priority-rejection learning, were proposed in this study. The incremental learning method is conceptually simple but still suffers from a large database of keywords for matching the testing template. The priority-rejection learning method can effectively reduce the matching time with a slight decrease in recognition accuracy. Regarding the unsupervised learning category, an automatic learning approach, called “most-matching learning,” which is based on priority-rejection learning, was developed in this study. Most-matching learning can be used to intelligently choose the appropriate utterances for system learning. The effectiveness and efficiency of all three proposed machine-learning approaches for DTW were demonstrated using keyword speech recognition experiments.