Improving linear prediction analysis of noisy speech by predictive noise cancellation

The analysis of speech using Linear Prediction is reformulated to account for the presence of acoustically added noise and a technique is presented for reducing its effect on parameter estimation. The method, called Predictive Noise Cancellation (PNC), modifies the noisy speech autocorrelations using an estimate of present background noise which is adaptively updated from an average all-pole noise spectrum. The all-pole noise spectrum is calculated by averaging autocorrelations during non-speech activity. The method uses procedures which are already available to the LPC analyzer, and thus is well suited for real time analysis of noisy speech. Preliminary results show signal to noise improvements on the order of 10 to 20 db.