Title :
A Bayesian Estimator for Non-intrusive Speech Quality Evaluation in Psychoacoustic Domain
Author :
Chen, Guo ; Parsa, Vijay
Author_Institution :
Dept. of Electr. & Comput. Eng., Western Ontario Univ., Ottawa, Ont.
Abstract :
A Bayesian estimator for non-intrusive speech quality evaluation is presented. In this novel speech quality estimator, the Gaussian mixture density hidden Markov models were used for characterizing different speech quality categories and the speech quality estimation was performed by using the Bayesian inference and minimum mean-squared error estimation. The performance of the proposed estimator was demonstrated by experimental evaluations in comparison with the standard ITU-T P.563 using speech coded databases
Keywords :
Bayes methods; Gaussian processes; hidden Markov models; mean square error methods; speech processing; Bayesian estimator; Bayesian inference; Gaussian mixture density hidden Markov models; minimum mean-squared error estimation; nonintrusive speech quality; psychoacoustic domain; Bayesian methods; Distortion measurement; Feature extraction; Hidden Markov models; Psychology; Speech analysis; Speech coding; Speech enhancement; Speech processing; Testing; Bayesian inference; Hidden Markov model; Pitch power density; Speech quality evaluation;
Conference_Titel :
Signal Processing and Information Technology, 2006 IEEE International Symposium on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9753-3
Electronic_ISBN :
0-7803-9754-1
DOI :
10.1109/ISSPIT.2006.270841
