Tone recognition of Vietnamese continuous speech using hidden Markov model

This paper presents our study on context independent tone recognition of Vietnamese continuous speech. Each of the six Vietnamese tones is represented by a hidden Markov model (HMM for short) and we used VNSPEECHCORPUS to learn these models in terms of fundamental frequency, F₀, and short-time energy. We focus on evaluating the influence of different factors on the tone recognition. The experimental results show that the best method to learn F₀ and energy is to use a logarithmic transformation function and then normalization with mean and mean deviation. In addition, we show that using 8 forms of tones and the discrimination between male and female speakers increase the accuracy of the Vietnamese tone recognition system.