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• W2116731278 abstract "This paper demonstrates the potential of theoretically motivated learning methods in solving the problem of non-intrusive quality estimation for which the state-of-the-art is represented by ITU-T P.563 standard. To construct our estimator, we adopt the speech features from P.563, while we use a different mapping of features to form quality estimates. In contrast to P.563 which assumes distortion-classes to divide the feature space, our approach divides the feature space based on a clustering which is learned from the data using Bayesian inference. Despite using weaker modeling assumptions, we are still able to achieve comparable accuracy on predicting mean-opinion-scores with P.563. Our work suggests Bayesian model-evidence as an alternative metric to correlation-coefficient for determining the necessary number of experts for modeling the data." @default.
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• W2116731278 date "2010-06-01" @default.
• W2116731278 modified "2023-10-14" @default.
• W2116731278 title "A bayesian hierarchical mixture of experts approach to estimate speech quality" @default.
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• W2116731278 doi "https://doi.org/10.1109/qomex.2010.5516203" @default.
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