Comparative study reveals superior accuracy and stability using a linearly mean-corrected minimum distance measure on speech spectra, indicating improved classification for speech processing.
An important consideration in speech processing involves classification of speech spectra. Several methods for performing this classification are discussed. A number of these were selected for comparative evaluation. Two measures of performance-accuracy and stability-were derived through the use of an automatic performance evaluation system. Over 3000 hand-labeled spectra were used. Of those evaluated, a linearly mean-corrected minimum distance measure, on a 40-point spectral representation with a square (or cube) norm was consistently superior to the other methods.
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Silverman et al. (1976) studied this question.
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