LPC Speech analysis using the L1norm

A new method is presented for low bit encoding. The approach taken is similar to that of LPC analysis in that an autoregressive model of the vocal tract is used. The estimate of the model is made using a least absolute value (LAV) criteria. That is the sum of the absolute values (L1norm) of the error is minimized. This approach is compared with the usual least squares (L2norm) methods i.e. covariance and autocorrelation [1]. The minimum L1estimate was obtained using the simplex method of linear programming [2,3]. It is well known that the L1norm estimate is highly robust to statistical outliers [4]. The robust nature of the minimum L1algorithm can be interrupted as an expectation that the residual will consist of a series of impulses. This expectation is of course valid for speech as demonstrated by the improved performance provided by multipulse LPC [5]. One of the disadvantages of multipulse LPC is the need for windowing [6]. This bandwidth doubling is not present in the L1norm method.