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In this paper, we discuss the issues in automatic recognition of vowels inPersian language. The present work focuses on new statistical method ofrecognition of vowels as a basic unit of syllables. First we describe a voweldetection system then briefly discuss how the detected vowels can feed torecognition unit. According to pattern recognition, Support Vector Machines(SVM) as a discriminative classifier and Gaussian mixture model (GMM) as agenerative model classifier are two most popular techniques. Currentstate-ofthe- art systems try to combine them together for achieving more powerof classification and improving the performance of the recognition systems. Themain idea of the study is to combine probabilistic SVM and traditional GMMpattern classification with some characteristic of speech like band-pass energyto achieve better classification rate. This idea has been analyticallyformulated and tested on a FarsDat based vowel recognition system. The resultsshow inconceivable increases in recognition accuracy. The tests have beencarried out by various proposed vowel recognition algorithms and the resultshave been compared.

Probabilistic SVM/GMM Classifier for Speaker-Independent Vowel Recognition in Continues Speech