Non-linear Adaptive Prediction of Speech with a Pipelined Recurrent Neural Network and a Linearised Recursive Least Squares Algorithm

نویسندگان

  • Jens Baltersee
  • Jonathon Chambers
چکیده

A novel linearised Recursive Least Squares (LRLS) learning algorithm is presented for an adaptive non-linear forward predictor based on a Pipelined Recurrent Neural Network (PRNN). Simulation studies with speech signals show that the non-linear predictor does not perform satisfactorily when the previously proposed stochastic gradient (SG) algorithm is used. However, significantly improved results are demonstrated with the new LRLS algorithm. The non-linear structure affords prediction gains that are approximately 2dB higher than those of a linear structure RLS based predictor.

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تاریخ انتشار 2007