NDLI: Investigating the use of speech features and their corresponding distribution characteristics for robust speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Shih-Hsiang Lin Yao-Ming Yeh Chen, B.
Copyright Year	2007
Description	Author affiliation: Nat. Taiwan Normal Univ., Taipei (Shih-Hsiang Lin; Yao-Ming Yeh; Chen, B.)
Abstract	The performance of current automatic speech recognition (ASR) systems often deteriorates radically when the input speech is corrupted by various kinds of noise sources. Quite a few of techniques have been proposed to improve ASR robustness over the last few decades. Related work reported in the literature can be generally divided into two aspects according to whether the orientation of the methods is either from the feature domain or from the corresponding probability distributions. In this paper, we present a polynomial regression approach which has the merit of directly characterizing the relationship between the speech features and their corresponding probability distributions to compensate the noise effects. Two variants of the proposed approach are also extensively investigated as well. All experiments are conducted on the Aurora-2 database and task. Experimental results show that for clean-condition training, our approaches achieve considerable word error rate reductions over the baseline system, and also significantly outperform other conventional methods.
Starting Page	87
Ending Page	92
File Size	210919
Page Count	6
File Format	PDF
ISBN	9781424417452
DOI	10.1109/ASRU.2007.4430089
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-09
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Automatic speech recognition Speech enhancement Probability distribution Polynomials Uncertainty Acoustic distortion Nonlinear distortion Noise robustness Histograms clustering speech recognition robustness histogram equalization polynomial regression
Content Type	Text
Resource Type	Article

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