A study of tone classification for continuous Thai speech recognition

This paper presents a study of tone classification for continuous Thai speech recognition. A modified autocorrelation algorithm was implemented with pitch detection, and the tone classifier utilized 3-layer feed-forward neural network with back-propagation. The best performance configuration of tone features was obtained with semi-tone scaling and mean-normalization producing a classification accuracy of 72.21%. Also, after considering the effects of final consonants, the average performance of the tone classifier improved to 77.13%. Experimental results showed that the pitch value of a tone with no final consonants has more variation than one with final consonants. Also the classification for tones with voiced final consonant gave better performance than tones with unvoiced final consonants.