Method of deriving characteristics values from a speech signal

Abstract

In a frequently used speech synthesis for voice output an excitation signal is applied to a number of resonators whose frequency and amplitude are adjusted in accordance with the sound to be produced. These parameters for adjusting the resonators may be gained from natural speech signals. Such parameters gained from natural speech signals may also be used for speech recognition, in which these parameter values are compared with comparison values. According to the invention, the parameters, particularly the formant frequencies, are determined by forming the power density spectrum via discrete frequencies from which autocorrelation coefficients are formed for consecutive frequency segments of the power density spectrum from which, in turn, error values are formed, while the sum of the error values is minimized over all segments and the optimum boundary frequencies of the segments are determined for this minimum. Via the autocorrelation coefficients, the LPC predictor coefficients can then be computed, from which coefficients the formant frequency is computed. The minimum of the error sum for the individual segments is found by way of dynamic programming, in which auxiliary values are initially computed from the power density spectrum and stored as Tables from which the autocorrelation coefficients are easily determined for individual frequency segments which are required for the computations in the dynamic programming process.