Tone recognition of continuous Thai speech under tonal assimilation and declination effects using half-tone model

This paper presents a method for continuous Thai tone recognition. One of the main problems in tone recognition is that several interacting factors affect F-0 realization of tones. In this paper, we focus on the tonal assimilation and declination effects. These effects are compensated by the tone in-formation of neighboring syllables, the F-0 downdrift and the context-dependent tone model. However, the context-dependent tone model is too large and its training time is very long. To overcome these problems, we propose a novel model called the half-tone model. The experiments, which compare all tone features and all tone models, were simulated by feedforward neural networks. The results show that the proposed tone features increase the recognition rates and the half-tone model outperforms conventional tone models, i.e. context-independent and context-dependent tone models, in terms of recognition rate and speed. The best results axe 94.77% and 93.82% for the inside test and outside test, respectively.