Paper Info
Title
The common vector approach and its comparison with other subspace methods in case of sufficient data
Abstract
This paper presents an application of the common vector approach (CVA), an approach mainly used for speech recognition problems when the number of data items exceeds the dimension of the feature vectors. The calculation of a unique common vector for each class involves the use of principal component analysis. CVA and other subspace methods are compared both theoretically and experimentally. TI-digit database is used in the experimental study to show the practical use of CVA for the isolated word recognition problems. It can be concluded that CVA results are higher in terms of recognition rates when compared with those of other subspace methods in training and test sets. It is also seen that the consideration of only within-class scatter in CVA gives better performance than considering both within- and between-class scatters in Fisher's linear discriminant analysis. The recognition rates obtained for CVA are also better than those obtained with the HMM method.
Year
DOI
Venue
2007
10.1016/j.csl.2006.06.002
Computer Speech & Language
Keywords
Field
DocType
practical use,sufficient data,cva result,isolated word recognition problem,subspace method,recognition rate,linear discriminant analysis,feature vector,speech recognition problem,better performance,common vector approach,speech recognition,word recognition,principal component analysis
Feature vector,Pattern recognition,Subspace topology,Computer science,Word recognition,Computational linguistics,Speech recognition,Artificial intelligence,Linear discriminant analysis,Hidden Markov model,Machine learning,Principal component analysis
Journal
Volume
Issue
ISSN
21
2
Computer Speech & Language
Citations 
PageRank 
References 
9
0.70
17
Authors
4
Name
Order
Citations
PageRank
M. Bilginer Gülmezoğlu116012.15
V. Dzhafarov2525.97
Rifat Edizkan3936.97
Atalay Barkana437416.23