Robust linear prediction for speech analysis

In this paper, a robust linear prediction algorithm is proposed. Rather than minimizing the sum of squared residuals as in the conventional linear prediction procedures, the robust LP procedure minimizes the sum of appropriately weighted residuals. The weight is a function of the prediction residual, and the cost function is selected to give more weight to the bulk of smaller residuals while de-weighting the small portion of large residuals. Based on Robustness Theory, the proposed algorithm will always give a more efficient (lower variance) estimate for the prediction coefficients if the excitation source is of Gaussian mixture such that a large portion of the excitations are from a normal distribution with a very small variance while a small portion of the excitations at the glottal openings and closures are from some unknown distribution with a much larger variance. The robust LP algorithm can be used in the front-end feature extractor for a speech recognition system and as an analyzer for a speech coding system. Testing on synthetic vowel data demonstrates that the robust LP procedure is able to reduce the formant and bandwidth error rate by more than an order of magnitude compared to the conventional LP procedures. Preliminary experiments on natural speech data indicate that the robust LP procedure is relatively insensitive to the placement of the LPC analysis window and to the value of the pitch period, for a given section of speech signal.