Robust linear predictive coding of speech

Abstract

In this thesis, a robust LPC vocoder system that can be operated in any environment is studied. In computing linear prediction coefficients representing the vocal tract, the effect of additive noise in the input speech is first removed by an autocorrelation subtraction method. In this method the noise autocorrelation is obtained or updated during nonspeech activity, assuming that noise is stationary. According to computer simulation results, when signal-to-noise ratio (SNR) of the input speech ranges from 0 to 10 dB, a performance improvement of about 5 dB can be gained by using this method. The proposed method is computationally very efficient and requires small storage area. Implementation of the proposed scheme in an LPC vocoder hardware that uses AMD 2903 bit-slice chips is also considered.