Automatic meter classification in Persian poetries using support vector machines

In this paper, a meter classification system has been proposed for Persian poems based on features extracted from uttered poem. In the first stage, the utterance has been segmented into syllables using three features, pitch frequency and modified energy of each frame of the utterance and its temporal variations. In the second stage, each syllable is classified into long syllable and short syllable classes which is a historically convenient categorization in Persian literature. In this stage, the classifier is an SVM classifier with radial basis function kernel and employed features are the syllable temporal duration, zero crossing rate and PARCOR coefficients of each syllable. The sequence of extracted syllables classes is then compared with classic Persian meter styles using dynamic time warping, to make the system robust against syllables insertion, deletion or classification. The system has been evaluated on 136 poetries utterances from 12 Persian meter styles gathered from 8 speakers, using k-fold evaluation strategy. The results show 91% accuracy in three top meter style choices of the system.