Abstract
The main aim of this study is to develop a low-complexity non-intrusive quality prediction model in Voice over Internet Protocol (VoIP) systems. In order to gain this goal, a 2-level structure for predicting the quality of speech is proposed. Furthermore, the capabilities of multi-gene genetic programming are investigated through developing a number of parallel models and different feature vectors. These models are utilized in two hierarchical levels to construct the final model. To consider the transmission media and speech signal characteristics in quality measurement process, both network impairments and per-frame features are employed simultaneously for developing models. Several experiments are performed based on the proposed structure while different combinations of speech feature types in the cases of noise free and noisy speech signals are examined. The obtained results indicate that using parallel models in a 2-level structure enhances the accuracy of derived models as compared with 1-level structure and common single-gene GP models.
Published Version
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