NDLI: Feature Fusion Applied to Missing Data ASR with the Combination of Recognizers

Content Provider	Springer Nature Link
Author	Joshi, Neil Guan, Ling
Copyright Year	2009
Abstract	Automatic speech recognition under adverse noise conditions has been a challenging problem. Under noise conditions when the stationarity assumption is valid, effective techniques have been established to provide excellent recognition accuracies. Under the conditions when this assumption cannot hold, recognition performance de- clines rapidly. Missing data, MD, theory is a promising method for robust automatic speech recognition, ASR, under an y noise condition. Unfortunately, the choice of feature used in the recognizer process is commonly limited to spectral based representations. The combination of recognizers approach to MD ASR allows the use of cepstral based features within the MD framework through a fusion of features mechanism in the pat- tern recognition stage. It was found that under two types of non-stationary noise conditions the combined fused effect, experienced by the fusion process, increased recognition accuracies substantially over traditional MD and cepstral based recognizers.
Starting Page	359
Ending Page	370
Page Count	12
File Format	PDF
ISSN	19398018
Journal	Journal of Signal Processing Systems
Volume Number	58
Issue Number	3
e-ISSN	19398115
Language	English
Publisher	Springer US
Publisher Date	2009-08-18
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Speech recognition Speech processing Hidden Markov models Pattern recognition Time Series Computer Imaging, Vision, Pattern Recognition and Graphics Pattern Recognition Image Processing and Computer Vision Electrical Engineering Circuits and Systems Signal, Image and Speech Processing
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Signal Processing Control and Systems Engineering Information Systems Modeling and Simulation Hardware and Architecture

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