On the optimal number estimation of selected features using joint histogram based mutual information for speech emotion recognition

Mutual information (MI) has been previously used to select the relevant features for the task of speech emotion recognition (SER). However, the procedure does not deliver the optimal number of relevant fea-tures. We propose MI based criterion for estimating this number defined as the minimum number of fea-tures that explains the variable of the class indices. In order to minimize the MI estimation errors, we also search the best histogram binning choice considering three formulas: Sturges, Scott and LMSE. Four selec-tion strategies MMI, CMI, JMI and TMI have been implemented and applied on 39-features vectors and on large dimension vectors. The feature selection results have been validated on independent text SER sys-tem, based on GMM classifier and evaluated on EMO-db database. Results demonstrate that LMSE bin choice gives the best MI estimation and ensures a minimal number of features with slight performance drop. Particularly, using the proposed stopping criterion, the CMI strategy achieves reduction of 48.72% in the case of the 39-features vectors size and 67.86% in the case of large dimension vectors. Moreover, using the recognition rate criterion, the JMI strategy gives a comparable feature reduction with slight improve-ment of performance but requiring very high computation capabilities. (c) 2019 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

A mutual information-based feature selection method was proposed in this study, aiming to minimize MI estimation errors by considering different histogram binning choices. Various selection strategies were implemented and applied on 39-features vectors and large dimension vectors. Results showed that LMSE bin choice provided the best MI estimation and ensured a minimal number of features, while the CMI strategy achieved significant reduction.