Sixth International Conference on Spoken Language Processing
(ICSLP 2000)

Beijing, China
October 16-20, 2000

Statistically Trained Orthographic to Sound Models for Thai

Ananlada Chotimongkol, Alan W. Black

Language Technologies Institute, Carnegie Mellon University, Pittsburgh, PA, USA

Many languages have a non-obvious, but not unrelated, relationship between orthography and pronunciation. Traditional methods for automatic conversion from letters to phones involve hand-crafted letter-to-sound rules, but these require care and expertise to develop. This paper presents a letter-to-sound rule system for Thai, that is trained automatically from lexicons. A statistical model, decision trees, is used to predict phones from letters. Letters mappping to multi-phones are used to solve the problem of implicit vowels and final consonants propagation and pre- and post-processing techniques are used to handle the inversion of initial consonants and vowels. For tone prediction, hand-crafted rules are used instead since there is no ambiguation if the phonological composition is known. Combining the n-gram of phone model with the decision trees, we can achieve 68.76% word accuracy which is better than 65.15% word accuracy in the rule-based approach.


Full Paper

Bibliographic reference.  Chotimongkol, Ananlada / Black, Alan W. (2000): "Statistically trained orthographic to sound models for Thai", In ICSLP-2000, vol.2, 551-554.