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We propose a probabilistic, non-intrusive method for quality assessment of speech that takes into consideration the bounded character of the preference scores. The quality ratings are modeled as iid Beta random variables, whose mean and precision are parametrized directly in terms of the signal features. Maximum likelihood estimation is used to learn the model parameters in view of a training database...
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...
A fundamental challenge in the design of objective models for estimation of speech signal quality lies in the shortage of subjectively labelled databases. This problem is particularly relevant when developing quality assessment models for wide-band (16 kHz sampling rate) signals where databases are scarce. We explore the possibility for seamlessly integrating a quality prior in the form of a narrow-band...
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