Real-time, non-intrusive speech quality estimation: A signal-based model

Abstract

Speech quality estimation, as perceived by humans, is of vital importance to proper functioning of telecommunications networks. Speech quality can be degraded due to various network related problems. In this paper we present a model for speech quality estimation that is a function of various time and frequency domain features of human speech. We have employed a hybrid optimization approach, by using Genetic Programming (GP) to find a suitable structure for the desired model. In order to optimize the coefficients of the model we have employed a traditional GA and a numerical method known as linear scaling. The proposed model outperforms the ITU-T Recommendation P.563 in terms of prediction accuracy, which is the current non-intrusive speech quality estimation model. The proposed model also has a significantly reduced dimensionality. This may reduce the computational requirements of the model. © 2008 Springer-Verlag Berlin Heidelberg.