Discrete Choice Models for Non-Intrusive Quality Assessment

QC 20130524
Non-intrusive signal quality assessment in general, and its application to speech signal processing, in particular, builds extensively upon statistical regression models. Commonly, the raw preference scores used for fitting these models belong to a categorical scale. Averaging the scores over a number of test subjects results in smooth, close-to-continuous ratings, thus justifying the use of regression as opposed to classification models. A form of marginalization, averaging subjective ratings takes away useful information about the reliability of individual test points. Using a model tailored to the raw data achieves highly competitive performance in terms of conventional performance measures while providing the additional advantage of identifying the usability of individual test points. In this paper, we consider the application of discrete choice models to non-intrusive quality assessment of speech.

Kleijn, W. BastiaanSchool of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand(Swepub:kth)u1bm0bvj (author)
De Vries, Bert (author)
KTHLjud- och bildbehandling (creator_code:org_t)

In:12th Annual Conference Of The International Speech Communication Association 2011 (INTERSPEECH 2011), Vols 1-5: ISCA, s. 200-2039781618392701

By the author/editor: Petkov, Petko N.; Kleijn, W. Basti ...; De Vries, Bert

About the subject

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Electrical Engin ...; and Signal Processin ...

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