An application of speech recognition with support vector machines

Abstract

Speech recognition systems aim to make human-machine communication quickly and easily. In recent years, various researches and studies have been carried out to develop speech recognition systems. Examples of these studies are speech recognition, speaker recognition and speaker verification. In this study, speech recognition systems were investigated, methods used in the literature were investigated and a Turkish speech recognition application was developed. The application consists of speech coding and speech recognition. Firstly 20 Turkish words which are frequently used on the computer were determined. There are 20 records from each word. A total of 400 words were recorded on the computer with a microphone. In the speech coding section of the application, these words recorded on the computer are encoded by the Linear Pre-estimation Coding (LPC) method and the LPC parameters for each word are obtained. In the speech recognition section of the application, the Support Vector Machines (SVM) method is used. Two types of SVM classifiers are designed. These are the Soft Margin SVM (SM-SVM) classifier and the Least Square SVM (LS-SVM) classifier. Classification consists of training and testing stages. Of the 400 coded words, 200 were used for the training phase and 200 were used for the testing phase. As a result, 91% accurate recognition success for the SM-SVM classifier; 71% correct recognition of the LS-SVM classifier has been achieved. © 2018 IEEE.