Use of center of gravity with the common vector approach in isolated word recognition

Authors:
M. Bilginer Gülmezoğlu;Rifat Edizkan;Semih Ergin;Atalay Barkana
Affiliations:
Eskişehir Osmangazi University, Electrical and Electronics Engineering Department, Meşelik Campus, 26480 Eskişehir, Turkey;Eskişehir Osmangazi University, Electrical and Electronics Engineering Department, Meşelik Campus, 26480 Eskişehir, Turkey;Eskişehir Osmangazi University, Electrical and Electronics Engineering Department, Meşelik Campus, 26480 Eskişehir, Turkey;Anadolu University, Electrical and Electronics Engineering Department, İki Eylül Campus, 26470 Eskişehir, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

Citing 3
Cited 0

Using Discriminant Eigenfeatures for Image Retrieval

IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition

IEEE Transactions on Pattern Analysis and Machine Intelligence
The common vector approach and its comparison with other subspace methods in case of sufficient data

Computer Speech and Language

Quantified Score

Hi-index	12.05

Visualization

Abstract

In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time axis, endpoint detection becomes extremely important for the recognition of isolated words. The COG method eliminates the need for endpoint detection. The effects of the COG method and a classical endpoint detection algorithm on the recognition rates of isolated words are investigated. The experimental results show that the COG method yields slightly higher recognition rates than the endpoint detection method in the TI-digit database when CVA is used.