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Using model reduction, a new approach for low-order speech modeling is presented. In this approach, the modeling process starts with a relatively high-order (full-order) autoregressive (AR) model obtained by some classical methods. The AR model is then reduced using the state projection method, operating in the state space. The model reduction yields a reduced-order autoregressive moving-average (...
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