NDLI: Speech recognition experiments using multi-span statistical language models

Content Provider	IEEE Xplore Digital Library
Author	Bellegarda, J.R.
Copyright Year	1999
Description	Author affiliation: Spoken Language Group, Apple Comput. Inc., Cupertino, CA, USA (Bellegarda, J.R.)
Abstract	A multi-span framework was proposed to integrate the various constraints, both local and global, that are present in the language. In this approach, local constraints are captured via n-gram language modeling, while global constraints are taken into account through the use of latent semantic analysis. The performance of the resulting multi-span language models, as measured by the perplexity, has been shown to compare favorably with the corresponding n-gram performance. This paper reports on actual speech recognition experiments, and shows that word error rate is also substantially reduced. On a subset of the Wall Street Journal speaker-independent, 20,000-word vocabulary, continuous speech task, the multi-span framework resulted in a reduction in average word error rate of up to 17%.
Starting Page	717
Ending Page	720
File Size	421853
Page Count	4
File Format	PDF
ISBN	0780350413
ISSN	15206149
DOI	10.1109/ICASSP.1999.759767
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1999-03-15
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Speech recognition Natural languages Vocabulary Error analysis Training data Data mining Displays Frequency Robustness History
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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