3 accepted papers
In this paper, we propose to apply ensemble learning methods on neural networks to improve the performance of speech emotion recognition tasks. The basic idea is to first divide unbalanced data set into balanced subsets and then combine the predictions of the models trained on these subsets. Several…
We propose a neural-network training algorithm that is robust to data imbalance in classification. In our proposed algorithm, weights are introduced to training examples, effectively modifying the trajectory traversed in the parameter space during the learning process. Furthermore, the proposed algo…
We propose to use orthogonal feature detectors in artificial neural networks for the robustness of performance under noisy conditions. The motivation is grounded on the principle that orthogonal decomposition is the most efficient among all representation of a signal. In this paper, we incorporate o…