Vietnamese speech recognition using Dynamic Time Warping and Coefficient of Correlation

The hidden Markov model is supposed as the most common and effective method used in speech recognition for all languages including Vietnamese. However, this method is quite cumbersome and difficult to implement in many embedded systems that have limited resources. Dynamic Time Warping (DTW) method, whereas, has been in much study by many scientists and is proved to be simple and efficient for a relatively small set of words (about 100 words). Though, this method has not been investigated for Vietnamese. This paper will present the investigation result of the combination between Dynamic Time Warping and Correlative Coefficient in Vietnamese speech recognition dependent with the speaker. The vocabulary to be recognized and trained are 124 words. The training data are recorded from 7 people (4 men and 3 women), with four recording time in noise free environment. The recognition outcome achieves the accuracy above 90% on average. In some control areas, such as computer and television control, this accuracy is very promising. DTW, therefore, is proposed as a simple and efficient for Vietnamese speech recognition in many simple control systems.