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      Emotion Recognition from Speech Signals by Using Evolutionary Algorithm and Empirical Mode Decomposition

      proceedings-article
      , , ,
      Electronic Visualisation and the Arts (EVA)
      Electronic Visualisation and the Arts
      9 - 13 July 2018
      Emotion recognition, Speech signals, Empirical mode decomposition, Genetic algorithm
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            Abstract

            Emotion status impacts massively the human’s health and the job performance of people. A system that can continuously and automatically monitor people’s emotion is worthy of development. Besides, the speech signals always contain some emotion features and are the most commonly used for communication between humans. The exploration of emotion recognition from speeches then becomes more important. In this paper, we propose a strategy for emotion recognition from speech by combining evolutionary algorithm (EA) with Empirical Mode Decomposition (EMD) to improve the emotion recognition rate. First, some emotional speeches were decomposed into several Intrinsic Mode Functions (IMFs) by using EMD process. The emotional part of a speech is then extracted by using these IMFs. In this paper, some weighted IMFs obtained from EMD are combined for the following recognition process. Hence, it is one of the goals of this paper to find the optimal weights corresponding to each IMF and to combine these weighted IMFs to make the recognition results as accurate as possible. The weights for each IMF are trained by evolutionary algorithm to find an optimal combination of IMFs. The reason why evolutional algorithm is used here is that evolutional algorithm always obtains some outstanding performances in many research concerning optimal design. The Mel-Frequency Cepstral Coefficients (MFCCs) are then computed and are used as the features for emotion recognition. An open database, eNTERFACE 2005 emotion database, is adopted in this paper as training and testing data for the experiments.

            Content

            Author and article information

            Contributors
            Conference
            July 2018
            July 2018
            : 140-147
            Affiliations
            [0001]Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C.
            [0002]Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C.
            [0003]Department of Information Engineering, I-Shou University, No.1, Sec. 1, Syuecheng Rd., Dashu District, Kaohsiung City 84001, Taiwan, R.O.C.
            Article
            10.14236/ewic/EVA2018.29
            660e190c-b47f-422d-a040-0a2ff0e3f7ef
            © Pan et al. Published by BCS Learning and Development Ltd. Proceedings of EVA London 2018, UK

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Electronic Visualisation and the Arts
            EVA
            London, UK
            9 - 13 July 2018
            Electronic Workshops in Computing (eWiC)
            Electronic Visualisation and the Arts
            History
            Product

            1477-9358 BCS Learning & Development

            Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2018.29
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            Applied computer science,Computer science,Security & Cryptology,Graphics & Multimedia design,General computer science,Human-computer-interaction
            Emotion recognition,Speech signals,Empirical mode decomposition,Genetic algorithm

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