On the optimal number estimation of selected features using joint histogram based mutual information for speech emotion recognition
| Autor: | Philippe Ravier, Abdenour Hacine-Gharbi |
|---|---|
| Přispěvatelé: | Laboratoire pluridisciplinaire de recherche en ingénierie des systèmes, mécanique et énergétique (PRISME), Université d'Orléans (UO)-Institut National des Sciences Appliquées - Centre Val de Loire (INSA CVL), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), IRAuS/Signal, Laboratoire Pluridisciplinaire de Recherche en Ingénierie des Systèmes, Mécanique et Energétique (PRISME), Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges (ENSI Bourges)-Université d'Orléans (UO)-Ecole Nationale Supérieure d'Ingénieurs de Bourges (ENSI Bourges) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
General Computer Science
Speech emotion recognition Computer science Binning of joint histogram Computation Feature selection 02 engineering and technology Features selection Histogram MFCC coefficients 0202 electrical engineering electronic engineering information engineering Emotion recognition ComputingMilieux_MISCELLANEOUS business.industry 020206 networking & telecommunications Pattern recognition Mutual information QA75.5-76.95 GMM models Electronic computers. Computer science 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing |
| Zdroj: | Journal of King Saud University-Computer and Information Sciences Journal of King Saud University-Computer and Information Sciences, Elsevier 2021, 33 (9), pp.1074-1083. ⟨10.1016/j.jksuci.2019.07.008⟩ Journal of King Saud University: Computer and Information Sciences, Vol 33, Iss 9, Pp 1074-1083 (2021) |
| ISSN: | 1319-1578 |
| DOI: | 10.1016/j.jksuci.2019.07.008⟩ |
| Popis: | 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 features. We propose MI based criterion for estimating this number defined as the minimum number of features 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 selection 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 system, 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 improvement of performance but requiring very high computation capabilities. |
| Databáze: | OpenAIRE |
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