Probabilistic non-intrusive quality assessment of speech for bounded-scale preference scores

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. Given a valuation point, the proposed model produces a distribution over the range of allowed quality ratings, which can be used to evaluate the statistics of interest. The model performance, in terms of correlation and root mean square error, compares favorably to the state-of-the-art in the field. Low computational complexity in training and prediction make the model attractive for a wide range of applications. The usage of band-based features in the feature set facilitates extension of the proposed model to input signals with larger bandwidth.