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.

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In this paper, the subspace method of pattern recognition, in which method classification is decided by the largest projection of an unknown pattern vector onto subspaces corresponding to different classes, is applied to the recognition of continuous Finnish speech.Classification is based on phonemic power spectra produced by an analog filter bank. When compared, e.g., with the nearest-neighbor method and the method of direction cosines, the advantages of the subspace method are an improved stability of classification and a more balanced total classification accuracy of the different phonemic classes with respect to their relative frequencies of occurrence. The efficient spanning of the subspaces as well as their mutual orthogonalization are discussed. Furthermore, the close relationship between the phonemic labeling and segmentation when using the subspace method is pointed Out.

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