Support Vector Machines for Thai Phoneme Recognition
Nuttakorn Thubthong · International Journal of Uncertainty Fuzziness and Knowledge-Based Systems · 2001
The Support Vector Machine (SVM) has recently been introduced as a new pattern classification technique. It learns the boundary regions between samples belonging to two classes by mapping the input samples into a high dimensional space, and seeking a separating hyperplane in this space. This paper describes an application of SVMs to two phoneme recognition problems: 5 Thai tones, and 12 Thai vowels spoken in isolation. The best results on tone recognition are 96.09 % and 90.57 % for the inside test and outside test, respectively, and on vowel recognition are 95.51 % and 87.08 % for the inside test and outside test, respectively.