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The IBM continuous speech recognition system has relied usually on 80- or 81-dimensional frequency-domain output every centisecond from the signal processing stages to generate its training and test data. On a controlled one-speaker data-base, a performance measure of 90.7 percent word-level accuracy was achieved using this type of data. For reasons of computational advantages, an investigation was carried out to determine a suitable method for reducing this dimensionality to 30 with minimal loss in accuracy. In one study, eigenvectors derived from the covariance matrix of 81- dimensional data were utilized to optimally rotate the data down to 30 dimensions. Two different variations of this experiment, the speaker-dependent and the speaker-independent cases, were attempted. In the other study, a more traditional approach of dividing the relevant frequency domain into 30 separate bands was investigated. The results of these studies indicated that the latter approach was marginally superior in performance to either of the two eigenvector techniques, and, in fact, accomplished the desired data reduction with no loss in accuracy.