Developments of Machine Learning Schemes for Dynamic Time-Wrapping-Based Speech Recognition. (28th November 2013)

• Record Type: Journal Article
• Authors: Ding, Ing-Jr; Yen, Chih-Ta; Hsu, Yen-Ming
• Other Names: Meen Teen-Hang Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2013(2013)
• Publication Date: 2013-11-28
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2013/542680
• Languages: English
• ISSNs: 1024-123X

Abstract

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.