Tone variation modeling for fluent Mandarin tone recognition based on clustering

Tone recognition for fluent Mandarin speech has always been a very difficult problem, because the complicated tone behavior is difficult to analyze. In this paper, a new method of modeling tone variation for fluent Mandarin tone recognition by clustering training data into few subsets and weighting the likelihood computed by inter-syllabic features (Lin et al. (2003)) is proposed. Experimental results indicate that the tone recognition accuracy can be improved significantly by this method and one modification of the method is robust and has less computation. Our tone variation modeling method is shown to improve the recognition rate from 91.3% to 95.2%.