A Study on Turkish Text - Dependent Speaker Recognition

Abstract

Speaker recognition is a pattern recognition task which has long been studied, but the accuracies are still far from the desired levels. The majority of the studies on speaker recognition demonstrates the results obtained from databases in which English voices are used. Since there are very few studies on Turkish speech, the performance of the known successful methods in Turkish voices are uncertain. Therefore, in this study, the performance on the Turkish text - dependent system is investigated by using Gaussian Mixture Model - Universal Background Model (GMM - UBM) method which is a well known method in speaker recognition systems. In the experimental studies, Turkish speaker recognition database consisting of 46 speakers (36 males and 10 females) is used. Equal error rate (EER) is used to measure system performance. The equal error rate for GMM - UBM method was found to be 5.73%. It has been observed in the experiments that the speaker verification performance of GMM - UBM classifier on Turkish database is encouraging.