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Monosyllabic Thai Tone Recognition Using Ant-Miner Algorithm


Saritchai Predawan, Chom Kimpan, Chai Wutiwiwatchai


Vol. 9  No. 1  pp. 227-235


Recognition of tone is essential for speech recognition and language understanding. A monosyllabic Thai tone recognition system, which is based on the Ant-Miner algorithm. The system is composed of three main process, fundamental frequency (F0) extraction from input speech signal, analysis of F0 contour for feature extraction, In the F0 feature extraction, the polynomial regression functions are employed to fit the segmented F0 curve where its coefficients are used as a feature vector[8]. In tone recognition, we used the Ant-Miner classifier to classify a tone by assuming that the feature is features vector. The hypothetical words used in this paper are composed of numerical words and monosyllabic Thai words. The experimental results show that by using the system as a speaker-dependent system, the maximum recognition rate is 96.20%. These results indicate that the intrinsic structure of tone can be exploited to reduce the need for costly labeled training data for tone learning and recognition.


Thai Tone Recognition, Ant-Miner Algorithm, Feature Extraction, Fundamental Frequency (F0)