Spectral classification of phonemes by learning subspaces

A new pattern recognition scheme with learning ability is introduced, and its application to the labeling of phonemes is reported. The basic classification algorithm is known as the subspace method in which classes of patterns are defined as linear vector subspaces spanned by the prototypes, and the class affiliation of an unknown pattern vector is decided by comparison of its orthogonal projections on the various subspaces. This method is here modified in two ways. In one of them, the prototype patterns are selected conditionally according to classification results obtained during training. In the second modification the subspaces are rotated in proper directions in the training procedure, depending on the classification results. By means of these methods, for the average accuracy of classification with 15 phonemic classes from continuous Finnish speech, a value of about 80 per cent was obtained.