Since speech sounds, such as fricative, glides, liquids, diphthongs, and transition regions between phones, reveal the most notable nonstationary nature, we propose the nonstationary autoregressive (AR) HMM with state-dependent polynomial function for modeling the nature of speech. Then, the nonstationary AR model has parameters depend on the states of the Markov chain. It is designed to handle the speech signal at the frame level, where it is represented by the signal, rather than dealing with feature vectors directly. Also, we proposed a new speech enhancement based on the nonstationary AR HMM and the IMM algorithm under white noise condition. The proposed enhancement is the weighted sum of the parallel Kalman filters with interacting rule by IMM algorithm. The simulation results shows that the proposed method offers performance gains relative to the previous results [7] with slightly increased complexity.
Cite as: Lee, K.Y., Rheem, J.Y. (1997) A nonstationary autoregressive HMM and its application to speech enhancement. Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997), 1407-1410, doi: 10.21437/Eurospeech.1997-392
@inproceedings{lee97c_eurospeech,
author={Ki Yong Lee and Jae Yeol Rheem},
title={{A nonstationary autoregressive HMM and its application to speech enhancement}},
year=1997,
booktitle={Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997)},
pages={1407--1410},
doi={10.21437/Eurospeech.1997-392}
}