Recognition of Greek Phonemes Using Support Vector Machines

In the present work we study the applicability of Support Vector Machines (SVMs) on the phoneme recognition task. Specifically, the Least Squares version of the algorithm (LS-SVM) is employed in recognition of the Greek phonemes in the framework of telephone-driven voice-enabled information service. The N-best candidate phonemes are identified and consequently feed to the speech and language recognition components. In a comparative evaluation of various classification methods, the SVM-based phoneme recognizer demonstrated a superior performance. Recognition rate of 74.2% was achieved from the N-best list, for N=5, prior to applying the language model.