Speech and Audio Signal Processing: Processing and Perception of Speech and Music
Speech and Audio Signal Processing: Processing and Perception of Speech and Music
The GA-P: A Genetic Algorithm and Genetic Programming Hybrid
IEEE Expert: Intelligent Systems and Their Applications
Lexicographic Parsimony Pressure
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Neural Network-Based Voice Quality Measurement Technique
ISCC '99 Proceedings of the The Fourth IEEE Symposium on Computers and Communications
Genetic Programming and Evolvable Machines
Vector quantization techniques for output-based objective speech quality
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 01
Real-time, non-intrusive evaluation of VoIP
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
Single-Ended Speech Quality Measurement Using Machine Learning Methods
IEEE Transactions on Audio, Speech, and Language Processing
Low-Complexity, Nonintrusive Speech Quality Assessment
IEEE Transactions on Audio, Speech, and Language Processing
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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.