Linear prediction analysis of speech with set-membership constraints: experimental results

Set-membership (SM) identification refers to a class of techniques for estimating parameters of linear system or signal models under a priori information which constrains the solutions to certain sets. When data do not help refine these membership sets, the effort of updating the parameter estimates at those points can be avoided. An application of the SM method to the problem of identifying the linear prediction (LP) parameters of speech is discussed, emphasizing experimental findings of practical significance.<
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