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

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.