Discrete choice models for non-intrusive quality assessment

P.N. Petkov, W.B. Kleijn, B. Vries, de

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

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.
Original languageEnglish
Title of host publicationProceeding of the 12th Annual Conference of the International Speech Communication Association (Interspeech 2011) 27-31 August 2011, Florence, Italy
EditorsP. Cosi, R. Mori, de, G. Fabbrizio, di, R. Pierracini
PublisherISCA
Pages193-196
Publication statusPublished - 2011
Eventconference; 12th Annual Conference of the International Speech Communication Association -
Duration: 1 Jan 2011 → …

Conference

Conferenceconference; 12th Annual Conference of the International Speech Communication Association
Period1/01/11 → …
Other12th Annual Conference of the International Speech Communication Association

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