Title
Multichannel Speech Enhancement Based on Speech Spectral Magnitude Estimation Using Generalized Gamma Prior Distribution
Abstract
We present multichannel speech enhancement method based on MAP speech spectral magnitude estimation using a generalized gamma model of speech prior distribution, where the model parameters are adapted from actual noisy speech in a frame-by-frame manner. The utilization of a more general prior distribution with its online estimation is shown to be effective for speech spectral estimation. We tested the proposed algorithm in an in-car speech database and obtained significant improvements on the speech recognition performance, particularly under nonstationary noise conditions such as music, air-conditioner and open window
Year
DOI
Venue
2006
10.1109/ICASSP.2006.1661177
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference
Keywords
Field
DocType
gamma distribution,maximum likelihood estimation,speech enhancement,speech recognition,MAP estimation,generalized gamma prior distribution,in-car speech database,multichannel speech enhancement,noisy speech,speech recognition performance,speech spectral magnitude estimation
Speech enhancement,Magnitude (mathematics),Spectral density estimation,Speech coding,Pattern recognition,Computer science,Speech recognition,Artificial intelligence,Gamma distribution,Speech recognition performance,Prior probability,Linear predictive coding
Conference
Volume
ISSN
ISBN
4
1520-6149
1-4244-0469-X
Citations 
PageRank 
References 
4
0.49
4
Authors
3
Name
Order
Citations
PageRank
Tran Huy Dat116525.31
Kazuya Takeda21301195.60
Fumitada Itakura343167.73