Adaptive total least squares based speech prediction

Abstract

In this paper, an instantaneous total error based adaptive linear predictor is presented for linear predictive coding (LPC) of speech signals. In LPC, the speech signal is predicted by a linear combination of delayed input signals that are contaminated by noise. For this reason, total least mean squares (T-LMS) algorithm is used to decode the noisy input signals and to predict a speech signal. A compressed speech prediction is done when the mean squares total error is minimized, showing the efficiency of T-LMS based LPC model. Experimental results are recorded for different values of signal to noise ratio (SNR) of the input signals, and a comparative study is presented with instantaneous error squares based adaptive filter. These results show the preference of proposed predictor over the other.