Modeling the perceived voice quality for VOIP system based on Neuro-fuzzy: A comparative study

This paper presents a comparative study for modeling the quality of VOIP based on one of the most commonly used intrusive method for assessing voice quality called PESQ. Intrusive measures of voice quality against the non-intrusive methods need original speech signal to do a comparison with degraded signal and measure perceived voice quality. The need to have original signal will limit this method for real-time traffic monitoring. Owing to this weakness, some efforts have been recently performed for modeling the voice quality according to speech and IP network parameters like packet loss, codec type, gender and language of talker. Among the past proposed methods, intelligent techniques such as neural networks have been very successful models. In this study our main intention is twofold: Firstly developing a nonintrusive Neuro-fuzzy model based on an intrusive method (PESQ) and secondly comparing the performance of Neuro-fuzzy model to other well-known intelligent modeling approaches. Several experimental results were done and reported for more illustration.