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 HMM (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 [6] with slightly increased complexity.
Cite as: Lee, Y., Lee, J., Lee, K.Y., Shirai, K. (2001) Speech enhancement based on IMM with NPHMM. Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001), 1875-1878, doi: 10.21437/Eurospeech.2001-443
@inproceedings{lee01d_eurospeech,
author={Yunjung Lee and Joohun Lee and Ki Yong Lee and Katsuhiko Shirai},
title={{Speech enhancement based on IMM with NPHMM}},
year=2001,
booktitle={Proc. 7th European Conference on Speech Communication and Technology (Eurospeech 2001)},
pages={1875--1878},
doi={10.21437/Eurospeech.2001-443}
}