Discriminative feature transformation by guided discriminative training
Roger Wend-Huu Hsiao, Brian Kan-Wing Mak · 2004
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 classification performance of only a subset of the classes at the possible expense of poorer classification performance of the remaining 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 the gradient descent method. To prove the concept, 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.