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

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      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.

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          Most cited references 20

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          Speech emotion recognition using hidden Markov models

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            Emotion recognition in human-computer interaction.

            In this paper, we outline the approach we have developed to construct an emotion-recognising system. It is based on guidance from psychological studies of emotion, as well as from the nature of emotion in its interaction with attention. A neural network architecture is constructed to be able to handle the fusion of different modalities (facial features, prosody and lexical content in speech). Results from the network are given and their implications discussed, as are implications for future direction for the research.
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              Statistical mechanics based on Renyi entropy

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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 140-147
                Affiliations
                Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C.
                Department of Electrical Engineering, National University of Kaohsiung, Kaohsiung 811, Taiwan, R.O.C.
                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
                © 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
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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