A new nonlinear prediction model based on the Recurrent Neural Predictive Hidden Markov Model for speech enhancement

In this paper, a new nonlinear prediction model based on the Recurrent Neural Predictive Hidden Markov Model (RNPHMM) is proposed for speech enhancement. Assuming that speech is an output of the RNPHMM combining RNN and HMM, the proposed nonlinear prediction model-based recurrent neural network (RNN) is used to present the nonlinear and nonstationary nature of speech. The RNPHMM is a nonlinear prediction process whose time-varying parameters are controlled by a hidden Markov chain. Given some speech data for training, the parameters of the RNPHMM are estimated by a learning algorithm based on the combination of Baum-Welch algorithm and RNN learning algorithm using the back-propagation algorithm. In our experiment, the proposed method achieved about 2-2.5 dB of improvement in SNR compared with both the NPHMM and the HIM at various input SNRs.