The Use of a Two-Pole Linear Prediction Model in Speech Recognition
BOLT BERANEK AND NEWMAN INC CAMBRIDGE MA
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In speech recognition applications, it is often desirable to make a gross characterization of the shape of the spectrum of a particular sound. The autocorrelation method of linear prediction analysis leads to an all-pole approximation to the signal spectrum. Hence an LPC analysis using two poles produces one possible gross characterization. The two poles are computed as the roots of a quadratic equation whose coefficients are the linear prediction parameters, which are simple functions of the autocorrelation coefficients R sub 0, R sub 1, and R sub 2. The poles are either both real or form a conjugate pair in the z plane. This fact, together with the exact positions of the poles, is particularly useful in describing certain gross characteristics of the spectrum. The spectral dynamic range of the two-pole spectrum and the normalized minimum error are suggested as more suitable substitutes for the two- pole bandwidths in interpreting the information supplied by the model for the purpose of spectral characterization.
- Voice Communications