Robust Speech Enhancement Based on NPHMM Under Unknown Noise

In this paper, a new speech enhancement based on the nonlinear H ∞ filtering and neural predictive HMM (NPHMM) is presented. In H ∞ filtering, no a priorknowledge of the noise source statistics is required. Speech is modeled as the output of a neural predictive HMM combining MLP neural network and HMM. The proposed enhancement method consists of multiple nonlinear H ∞ filters with parameter of the NPHMM. The switching between the nonlinear H ∞ filters is governed by a finite state Markov chain according to the transition probabilities. An approximate improvement of 0.4-1.8dB in output SNR is achieved at various input SNRs compared with conventional Kalman method.