A SVM Based Speech to Text Converter for Turkish Language

Abstract

In proposed speech to text conversion, a Support Vector Machines (SVM) based Turkish speech to text converter system has been developed. In the recognition system, Mel Frequency Cepstral Coefficients (MFCC) has been applied to extract features of Turkish speech and SVM based classifier has been used to classify the phonemes. The morphological structure of Turkish, a language based on phonemes, has been taken into consideration in the devoloped person-dependent voice recognition system. Unlike the multiclass classifiers which are used in the SVM-MFCC based voice recognition system, a new SVM classifier system has been developed that uses fewer classes in layers, increasing the number of multiclass layers. A new Text Comparison Algorithm is proposed, which also uses phoneme sequence to measure similarity in word similarity measurement. Along with these enhancements, as the training period becomes higher, performance of voice recognition is improved and word recognition performance is increased. The performance of the proposed structure is compared with similar systems.