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.
[1]
Doh-Suk Kim,et al.
ANIQUE+: A new American national standard for non-intrusive estimation of narrowband speech quality
,
2007,
Bell Labs Technical Journal.
[2]
Radford M. Neal.
Pattern Recognition and Machine Learning
,
2007,
Technometrics.
[3]
S. Chib,et al.
Bayesian analysis of binary and polychotomous response data
,
1993
.
[4]
METHODS FOR SUBJECTIVE DETERMINATION OF TRANSMISSION QUALITY Summary
,
2022
.
[5]
Methods for objective and subjective assessment of quality Perceptual evaluation of speech quality ( PESQ ) : An objective method for end-to-end speech quality assessment of narrow-band telephone networks and speech codecs
,
2002
.
[6]
W. Bastiaan Kleijn,et al.
Probabilistic non-intrusive quality assessment of speech for bounded-scale preference scores
,
2010,
2010 Second International Workshop on Quality of Multimedia Experience (QoMEX).
[7]
Refik Soyer,et al.
Bayesian Methods for Nonlinear Classification and Regression
,
2004,
Technometrics.
[8]
R. Tibshirani,et al.
Least angle regression
,
2004,
math/0406456.