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Generalized Maximal Margin Discriminant Analysis for Speech Emotion Recognition

Jin Y., Zheng W., Zhao L., Yan J.

Przegląd Elektrotechniczny

2013

R. 89, nr 5

86-91

A novel speech emotion recognition method based on the generalized maximum margin discriminant analysis (GMMDA) method is proposed in this paper. GMMDA is a multi-class extension of our proposed two-class dimensionality reduction method based on maximum margin discriminant analysis (MMDA), which utilizes the normal direction of optimal hyperplane of linear support vector machine (SVM) as the projection vector for feature extraction. To generate an optimal set of projection vectors from MMDA-based dimensionality reduction method, we impose orthogonal restrictions on the projection vectors and then recursively solve the problem. Moreover, to deal with the multi-class speech emotion recognition problem, we present two recognition schemes based on our proposed dimensionality reduction approach. One is using “one-versus-one" strategy for multi-class classification, and the other one is to compose the projection vectors of each pair of classes to obtain a transformation matrix for the multi-class dimensionality reduction.

W artykule przedstawiono metodę analizy emisji głosu pod kątem rozpoznawania emocji. Rozwiązanie bazuje na analizie dyskryminacyjnej maksymalnego marginesu GMMDA.

A Galerkin approximation method including space dimensional reduction - applied for solution of a heat conduction equation

Nakonieczny K.

Applied Computer Science

2012

Vol. 8, no 2

48--58

A multivariate data fitting procedure, based on the Galerkin minimization method, is studied in this paper. The main idea of the developed approach consists in projecting the set of data points from the original, higher-dimensional space, onto a line section. Then, the approximation problem is solved in the resulting one-dimensional space. The elaborated recipe can be designed so that it is computationally more efficient than the schemes based on the least squares minimization. The performance of the method is studied by comparison with the least squares and the moving least squares procedures in a number of examples, including the solution of the heat diffusion equation.