Linear prediction analysis of speech based on set-membership theory

Abstract

When computing linear prediction (LP) parameters of speech, large numbers of data are uninformative in a certain set-theoretic sense, and the expense of updating the estimates at these times can be avoided. “Set-membership” (SM) identification is formulated as a weighted recursive covariance LP problem with a special criterion for dynamic weight determination. An algorithm is developed which can be implemented on a systolic processor if desired, but which retains a simple interpretation as a specially weighted convariance LP method. The algorithm is applied to identification of the LP parameters of real speech data, and a number of practical issues are discussed. The potential for an adaptive strategy and other open research questions generated by the experimental work are discussed in the conclusions.