DocumentCode :
695647
Title :
Exploiting local and global structures for TIMIT phone classification
Author :
Heyun Huang ; ten Bosch, Louis ; Gemmeke, Jort ; Cranen, Bert ; Boves, Lou
Author_Institution :
Dept. of Linguistics, Radboud Univ. Nijmegen, Nijmegen, Netherlands
fYear :
2011
fDate :
Aug. 29 2011-Sept. 2 2011
Firstpage :
1485
Lastpage :
1489
Abstract :
Using contextual information of phones is an effective way to improve the performance of phone classification tasks, but requires the use of dimensionality reduction. One of the disadvantages of Linear Discriminant Analysis (LDA), a popular dimensionality reduction method is that it is not able to account for local differences between the distributions of classes in the feature space. Newer methods, such as the Local Fisher Discriminant Analysis (LFDA), on the other hand, may overestimate the contribution of local distributions. In this paper, we propose to use a dimensionality reduction algorithm with an affinity matrix that allows finding the optimal trade-off between local and global information. Experiments on TIMIT show that both local and global information in the MFCC feature space are important for phone classification and that a substantial improvement can be achieved over both LDA and LFDA.
Keywords :
cepstral analysis; feature extraction; matrix algebra; signal classification; speech processing; LDA; LFDA; MFCC feature space; TIMIT phone classification; affinity matrix; contextual information; dimensionality reduction method; linear discriminant analysis; local fisher discriminant analysis; Accuracy; Mel frequency cepstral coefficient; Optimization; Speech; Speech recognition; Trajectory; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference, 2011 19th European
Conference_Location :
Barcelona
ISSN :
2076-1465
Type :
conf
Filename :
7074197
Link To Document :
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