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Author Hsiao, Roger ♦ Mak, Brian
Source CiteSeerX
Content type Text
File Format PDF
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Description In this paper, we investigate guided discriminative training in the context of improving multi-class classification problems. We are interested in applications that require improvement in the classi-fication performance of only a subset of the classes at the possi-ble expense of poorer classification performance of the remain-ing classes. However, should the classification of the remaining classes deteriorate, it is guaranteed not to be worse than the extent that the user specifies. The problem is formulated as a nonlinear programming problem, which can be translated to a unconstrained nonlinear optimization problem using the barrier method that, in turn, can be solved by gradient descent method. To prove the con-cept, we apply guided discriminative training to derive an optimal linear transformation on the mel-filterbank log power spectra to improve TIMIT phoneme classification. Encouraging results are obtained. 1.
Proc. ICASSP-04, Montreal
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article
Publisher Date 2004-01-01