Standard Yorùbá Context Dependent Tone Identification Using Multi-Class Support Vector Machine (MSVM)

Most state-of-the-art large vocabulary continuous speech recognition systems employ context dependent (cd) phone units, however, the cd phone units are not efficient in capturing long-term spectral dependencies of tone in most tone languages. the standard yorùbá (sy) is a language composed of syllable with tones and requires different method for the acoustic modeling. in this paper, a context dependent tone acoustic model was developed. tone unit is assumed as syllables, amplitude magnified difference function (amdf) was used to derive the utterance wide f_0 contour, followed by automatic syllabification and tri-syllable forced alignment with speech phonetization alignment and syllabification sppas tool. for classification of the context dependent (cd) tone, slope and intercept of f_0 values were extracted from each segmented unit. supervised clustering scheme was utilized to partition cd tri-tone based on category and normalized based on some statistics to derive the acoustic feature vectors. multi-class support vector machine (msvm) was used for tri-tone training. from the experimental results, it was observed that the word recognition accuracy obtained from the msvm tri-tone system based on dynamic programming tone embedded features was comparable with phone features. a best parameter tuning was obtained for 10-fold cross validation and overall accuracy was 97.5678%. in term of word error rate (wer), the msvm cd tri-tone system outperforms the hidden markov model tri-phone system with wer of 44.47%.