A study on IMM with NPHMM and an application to speech enhancement

Abstract

The nonlinear speech enhancement method with interactive parallel-extended Kalman filter is applied to speech contaminated by additive white noise. To represent the nonlinear and nonstationary nature of speech, we assume that speech is the output of a nonlinear prediction hidden Markov models (NPHMM) combining both neural network and HMM. The NPHMM is a nonlinear autoregressive process whose time-varying parameters are controlled by a hidden Markov chain. The simulation results shows that the proposed method offers better performance gains relative to the previous results (Signal Process 65 (1998) 373) with slightly increased complexity. (C) 2004 Elsevier B.V. All rights reserved.