Title :
On the dimensionality of steady-state vowel normalization
Author :
Friedman, David H.
Author_Institution :
The MITRE Corporation, Bedford, MA
Abstract :
Vowel classification is considered from the viewpoint of cluster separation in a vector space, with Mahalanobis distance as the criterion. The number of significant axes of variation needed to characterize each speaker, weighted with respect to cluster separation, is found from actual formant data to be on the order of four, and the potential improvement in separation accountable to structure in the data is estimated at about 3 db by comparison with results for the same procedure applied to random data.
Keywords :
Character recognition; Data mining; Density functional theory; Frequency estimation; Shape; Speech recognition; Steady-state;
Conference_Titel :
Acoustics, Speech, and Signal Processing, IEEE International Conference on ICASSP '86.
DOI :
10.1109/ICASSP.1986.1168760
