Degradation-classification assisted single-ended quality measurement of speech

We propose an algorithm to classify speech degradations at network endpoints and to estimate the speech quality based on the degradation classification decision. Perceptual features from degraded speech signals are used to form statistical reference models of different degradation classes. Consistency measures, calculated between degraded speech signals and the reference models, are used to train a degradation classifier and mean opinion score (MOS) mappings. The quality of a received speech signal is estimated based on its degradation class and the MOS mapping associated with the class. Experimental results show that the proposed algorithm achieves high classification accuracy, and degradation classification improves the accuracy of the quality estimate.