Iterative speech enhancement using a non-linear dynamic state model of speech and its parameters

被引：0
|
作者：
Windmann, Stefan [1 ]
Haeb-Umbach, Reinhold [1 ]
机构：
[1] Univ Paderborn, Dept Commun Engn, D-33098 Paderborn, Germany
来源：
2006 IEEE International Conference on Acoustics, Speech and Signal Processing, Vols 1-13 | 2006年
关键词：
D O I：
暂无
中图分类号：
O42 [声学];
学科分类号：
070206 ; 082403 ;
摘要：
A marginalized particle filter is proposed for performing single channel speech enhancement with a non-linear dynamic state model. The system consists of a particle filter for tracking line spectral pair (LSP) parameters and a Kalman filter per particle for speech enhancement. The state model for the LSPs has been learnt on clean speech training data. In our approach parameters and speech samples are processed at different time scales by assuming the parameters to be constant for small blocks of data. Further enhancement is obtained by an iteration which can be applied on these small blocks. The experiments show that similar SNR gains are obtained as with the Kalman-EM-iterative algorithm. However better values of the noise level and the log-spectral distance are achieved.
引用
收藏
页码：465 / 468
页数：4
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