September 22-25, 1997
Several methods of measuring the class separability in a feature space used to model speech sounds are described. A simple one-dimensional feature space is considered first where class discrimination is measured using the F-ratio. Using a conventional feature set comprising static, velocity and acceleration MFCCs a ranking of the discriminative ability of each coefficient is made for both a digit and alphabet vocabulary. These rankings are shown to be quite similar for the two vocabularies. Discrimination measures are extended to multi- dimensional feature spaces using the J-measures. It is postulated that high correlation exists between feature sets which have a good measured class discrimination and those which give good recognition accuracy. Experiments are presented which measure this correlation and use it to predict recognition accuracy for a given set of features. These estimates are shown to be accurate for previously unseen combinations of features. A brief analysis of the effect linear discriminant analysis on the feature space is made using these measures of separability. It is shown that LDA and separability measures are closely linked.
Bibliographic reference. Nicholson, Simon / Milner, Ben / Cox, Stephen (1997): "Evaluating feature set performance using the f-ratio and j-measures", In EUROSPEECH-1997, 413-416.