Discriminative metric design for robust pattern recognition

Motivated by the development of discriminative feature extraction (DFE), many researchers have come to realize the importance of designing a front-end feature extraction unit with an appropriate link to backend classification. This paper proposes an advanced formalization of DFE, which we call the discriminative metric design (DMD), and elaborates on its exemplar implementation by using a simple, linear feature transformation matrix. The resulting DMD implementation is shown to have a close relationship to various discriminative pattern recognizers, including artificial neural networks. The utility of the proposed method is clearly demonstrated in speech pattern recognition experiments.