Thai Spelling Recognition Using a Continuous Speech Corpus

Spelling recognition is an approach to enhance a speech recognizer’s ability to cope with incorrectly recognized words and out-of-vocabulary words. This paper presents a general framework for Thai speech recognition enhanced with spelling recognition. In order to implement Thai spelling recognition, Thai alphabets and their spelling methods are analyzed. Based on hidden Markov models, we propose a method to construct a Thai spelling recognition system by using an existing continuous speech corpus. To compensate the difference between spelling utterances and continuous speech utterances, the adjustment of utterance speed is taken into account. Assigning different numbers of states for syllables with different durations is helpful to improve the recognition accuracy. Our system achieves up to 79.38% accuracy.