  In this paper, we discuss the issues in automatic recognition of vowels in
Persian language. The present work focuses on new statistical method of
recognition of vowels as a basic unit of syllables. First we describe a vowel
detection system then briefly discuss how the detected vowels can feed to
recognition unit. According to pattern recognition, Support Vector Machines
(SVM) as a discriminative classifier and Gaussian mixture model (GMM) as a
generative model classifier are two most popular techniques. Current
state-ofthe- art systems try to combine them together for achieving more power
of classification and improving the performance of the recognition systems. The
main idea of the study is to combine probabilistic SVM and traditional GMM
pattern classification with some characteristic of speech like band-pass energy
to achieve better classification rate. This idea has been analytically
formulated and tested on a FarsDat based vowel recognition system. The results
show inconceivable increases in recognition accuracy. The tests have been
carried out by various proposed vowel recognition algorithms and the results
have been compared.
