NDLI: Double Gaussian based feature normalization for robust speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Bo Liu Li-Rong Dai Jin-Yu Li Ren-Hua Wang
Copyright Year	2004
Description	Author affiliation: Univ. of Sci. & Technol. of China, Anhui, China (Bo Liu; Li-Rong Dai; Jin-Yu Li; Ren-Hua Wang)
Abstract	In this paper, a new feature normalization approach, based on the cumulative density function (CDF) matching principle, is proposed. Since speech features in noisy environments usually follow bimodal distributions, we fully utilize this characteristic by representing the CDF of the features with a double Gaussian model. A feature normalization process is performed according to the estimated CDF. The experimental results on the Aurora2 database show that the performance of our method is much better than that of the conventional mean and variance normalization (MVN) method, and comparable to that of the method combining spectral subtraction and histogram equalization (HE). Moreover, further improvement has been gained by combining our method with a simple temporal feature smoothing process. This result suggests that our new method has the potential to be integrated with other techniques to provide even better performance.
Starting Page	253
Ending Page	256
File Size	348889
Page Count	4
File Format	PDF
ISBN	0780386787
DOI	10.1109/CHINSL.2004.1409634
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-12-15
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Histograms Working environment noise Speech recognition Speech enhancement Density functional theory Robustness Spatial databases Parametric statistics Helium Testing
Content Type	Text
Resource Type	Article

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