Using Bayesian classifiers for estimating quality of VOIP

In real-time multi-media services, that uses internet infrastructure for transferring data traffics, the quality of service and consequently the level of user satisfaction are significant parameters. Our objective in this paper is to investigate the capability of Bayesian classifiers for estimating the quality of perceived voice in VoIP (Voice over IP) system. In this study, some quality parameters have been utilized to estimate the level of user satisfaction. The employed classifiers operate non-intrusively that means there is no need for original signal to estimate the quality of the perceived voice. For this purpose, a data set has been provided by simulation environment based on PESQ (that is an intrusive method that compares original and degraded signal for evaluating the quality of voice). Finally, we compare the performance of Bayesian classifiers with some other classification approaches in terms of estimating accuracy. For this purpose, the WEKA tool is used that contains implementation of many algorithms for classification problems. The results obtained, show the efficiency of Bayesian classifiers comparing to the other methods in terms of accuracy and computational time.