Autoregressive model-based speech packet-loss concealment

We study packet-loss concealment for speech based on autoregressive modeling using a rigorous minimum mean square error (MMSE) approach. The effect of the model estimation error on predicting the missing segment is studied and an upper bound on the mean square error is derived. Our experiments show that the upper bound is tight when the estimation error is less than the signal variance. We also consider the usage of perceptual weighting on prediction to improve speech quality. A rigorous argument is presented to show that perceptual weighting is not useful in this context. We create simple and practical MMSE-based systems using two signal models: a basic model capturing the short-term correlation and a more sophisticated model that also captures the long-term correlation. Subjective quality comparison tests show that the proposed MMSE-based system provides state-of-the-art performance.