Abstract. Emotional speech recognition is a multidisciplinary research area that has received increasing attention over the last few years. The present paper considers the application of restricted Boltzmann machines (RBM) and deep belief networks (DBN) to the difficult task of automatic Spanish emotional speech recognition. The principal motivation lies in the success reported in a growing body of work employing these techniques as alternatives to traditional methods in speech processing and speech recognition. Here a well-known Spanish emotional speech database is used in order to extensively experiment with, and compare, different combinations of parameters and classifiers. It is found that with a suitable choice of parameters, RBM and DBN can achieve comparable results to other classifiers.

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It is important to make sense of the data within its context to propose a useful model to solve a problem. This domain knowledge includes information not contained in the data, but that will help us understand the data to be fed into a machine-learning algorithm and guide us on what features might help our model. Nevertheless, domain knowledge may become insufficient as the input variables increase, forcing the need to try automated feature selection techniques. In this study, we investigate whether the joint use of 1) feature selection techniques, such as Chi-square, Tree-based Feature Selection, Pearson’s Correlation, LASSO, Low Variance, and Recursive Feature Elimination, 2) outlier detection methods such as Isolation-Forest, and 3) Cross-Validation techniques lead to improving the accuracy in multiclass classification in machine learning. Specifically, we address the classification of patterns representing the activation state of cell signaling components into classes that symbolize the different cellular processes triggered in cancer cells. The results presented in this work have shown an accuracy increase with up to 80% fewer input features by only using 3 out of the 16 original descriptors.

One of the major challenges in the area of artificial neural networks is the identification of a suitable architecture for a specific problem. Choosing an unsuitable topology can exponentially increase the training cost, and even hinder network convergence. On the other hand, recent research indicates that larger or deeper nets can map the problem features into a more appropriate space, and thereby improve the classification process, thus leading to an apparent dichotomy. In this regard, it is interesting to inquire whether independent measures, such as mutual information, could provide a clue to finding the most discriminative neurons in a network. In the present work we explore this question in the context of Restricted Boltzmann Machines, by employing different measures to realize posttraining pruning. The neurons which are determined by each measure to be the most discriminative, are combined and a classifier is applied to the ensuing network to determine its usefulness. We find that two measures in particular seem to be good indicators of the most discriminative neurons, producing savings of generally more than 50\% of the neurons, while maintaining an acceptable error rate. Further, it is borne out that start-M\' aximo S\' anchez-Guti\' errez \cdot John Goddard Close Departamento de Ingenier\' {\i} a El\' ectrica, Universidad Aut\' onoma Metropolitana -Iztapalapa (M\' exico).