A Quality Estimation of Synthesized Speech Transmitted over IP Networks

Abstract

A design of the parametric models estimating a quality of synthesized speech transmitted through IP networks is presented in this paper. A Genetic Programming and Random Neural Network as machine learning techniques were deployed to design the models. A set of the quality-affecting parameters was used as an input to the designed parametric estimation models in order to estimate a quality of synthesized speech transmitted over IP networks (VoIP environment). The performance results obtained for the designed parametric estimation models have validated both genetic programming and random neural network as powerful techniques, delivering good accuracy and generalization ability; this makes them perspective candidates for quality estimation of this type of speech in the corresponding environment. The developed parametric models can be helpful for network operators and service providers in a planning phase or early-development stage of telecommunication services based on synthesized speech.

Keywords

• genetic programming
• random neural network
• speech quality estimation
• synthesized speech
• packet loss
• speech codec