DocumentCode
1499452
Title
Improved speech recognition using a subspace projection approach
Author
Loizou, Philipos C. ; Spanias, Andreas S.
Author_Institution
Dept. of Appl. Sci., Arkansas Univ., Little Rock, AR, USA
Volume
7
Issue
3
fYear
1999
fDate
5/1/1999 12:00:00 AM
Firstpage
343
Lastpage
345
Abstract
Two class separability criteria based on the divergence measure are proposed to improve speech recognition performance. The average and weighted average divergence measures are used as criteria for finding a transformation matrix which maps the original features into a more discriminative subspace. Results are presented for a highly confusable task
Keywords
matrix algebra; probability; speech recognition; average divergence measures; class separability criteria; divergence measure; highly confusable task; speech recognition; subspace projection approach; transformation matrix; weighted average divergence measures; Covariance matrix; Degradation; Density functional theory; Hidden Markov models; Maximum likelihood estimation; Optimization methods; Speech recognition; Telephony; Vocabulary; Weight measurement;
fLanguage
English
Journal_Title
Speech and Audio Processing, IEEE Transactions on
Publisher
ieee
ISSN
1063-6676
Type
jour
DOI
10.1109/89.759044
Filename
759044
Link To Document