Abstract
The speech signal provides rich information about the speaker’s emotional state. Therefore, recognition of the emotion in speech has become one of the research themes in the processing of speech and applications based on human-computer interaction. This article provides an experimental study and examines the detection of negative emotions such as fear and anger with regard to the neutral emotional state. The data set is collected from speeches recorded in the Moroccan Arabic dialect. Our aim is first to study the effects of emotion on the selected acoustic characteristics, namely the first four formants F1, F2, F3, F4, the fundamental frequency F0, Intensity, Number of pulses, Jitter and Shimmer and then compare our results to previous works. We also study the influence of phonemes and speaker gender on the relevance of these characteristics in the detection of emotion. To this aim, we performed classification tests using the WEKA software. We found that F0, Intensity, Number of Pulses have the best rates of recognition regardless speaker gender and phonemes. Moreover the second and third formant are the features that highlighted phoneme’s effect.
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Aggarwal, R.K., Dave, M.: Performance evaluation of sequentially combined heterogeneous feature streams for Hindi speech recognition system. Telecommun. Syst. 52(3), 1457–1466 (2013)
Albornoz, E.M., Milone, D.H., Rubner, H.L.: Spoken emotion recognition using hierarchical classifiers. Comput. Speech Lang. 25(3), 556–570 (2011)
Banse, R., Scherer, K.R.: Acoustic profiles in vocal emotion expression. J. Personal. Soc. Psychol. 70(3), 614–636 (1996)
Batliner, A., Schuller, B., Seppi, D., Steidl, S., Devillers, L., Vidrascu, L., Vogt, T., Aharonson, V., Amir, N.: The automatic recognition of emotions in speech. In: Emotion-Oriented Systems, pp. 71–99. Springer, Heidelberg (2011)
Boersma, P.: Praat, a system for doing phonetics by computer. Glot Int. 5(9/10), 341–345 (2001)
Burkhardt, F.: Simulation emotionaler Sprechweise mit Sprachsynthese verfahren. Ph.D. thesis, TU Berlin (2001)
Busso, C., Narayanan, S.S.: Interrelation between speech and facial gestures in emotional utterances: a single subject study. IEEE Trans. Audio Speech Lang. Process. 10(20), 1–16 (2007)
Busso, C., Narayanan, S.S.: Joint analysis of the emotional fingerprint in the face and speech: a single subject study. In: International Workshop on Multimedia Signal Processing (MMSP), Chanée, Grèce, pp. 43–47. IEEE, Octobre 2007
Wu, C.H., Liang, W.B.: Emotion recognition of affective speech based on multiple classifiers using acoustic-prosodic information and semantic labels. IEEE Trans. Affect. Comput. 2(1), 1–21 (2012)
Clore, G.L.: Why emotions are felt. In: Ekman, P., Davidson, R.J. (eds.) The Nature of Emotion: Fundamental Questions, pp. 103–111. Oxford University Press, New York (1994)
Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., Taylor, J.G.: Emotion recognition in human-computer interaction. IEEE Signal Process. Mag. 18(1), 32–80 (2001)
Damasio, A.: L’erreur de Descartes. Grosset/Putnam, New York (1994)
Davletcharova, A., Sugathan, S., Abraham, B., James, A.P.: Detection and analysis of emotion from speech signals. Procedia Comput. Sci. 58, 91–96 (2015)
Ekman, P.: Expression and the nature of emotion. In: Scherer, K.R., Ekman, P. (eds.) Approaches to Emotion, pp. 319–343. Lawrence Erlbaum Associates, Hillsdale (1984)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)
Hartmann, K., Siegert, I., Philippou-Hübner, D., Wendemuth, A.: Emotion detection in HCI: from speech features to emotion space. In: 12th IFAC Symposium on Analysis, Design, and Evaluation of Human-Machine Systems, Las Vegas, NV, USA, 11–15 August 2013
Hosmer, D.W., Lemeshow, S., Sturdivant, R.X.: Applied Logistic Regression, 3rd edn. Wiley, Hoboken (2013). ISBN 978-0470-58247-3
Huang, C., Gong, W., Fu, W., Feng, D.: A research of speech emotion recognition based on deep belief network and SVM. Math. Probl. Eng. (2014)
Johnstone, T., Scherer, K.R.: Vocal communication of emotion. In: Lewis, M., Haviland-Jones, J.M. (eds.) Handbook of Emotions, pp. 220–235. Guilford, New York (2000)
Juslin, P.N., Laukka, P.: Communication of emotions in vocal expression and music performance: different channels, same code. Psychol. Bull. 129(5), 770–814 (2003)
Koolagudi, S.G., Rao, K.S.: Emotion recognition from speech: a review. Int. J. Speech Technol. 15, 99–117 (2012)
Mower, E., Matarić, M.J., Narayanan, S.: A framework for automatic human emotion classification using emotion profiles. IEEE Trans. Audio Speech Lang. Process. 19(5), 1057–1070 (2011)
Murray, I.R., Arnott, J.L.: Toward the simulation of emotion in synthetic speech: a review of the litterature on human vocal emotion. J. Acoust. Soc. Am. 93(2), 1097–1108 (1993)
Oudeyer, P.: The production and recognition of emotions in speech: features and algorithms. Int. J. Hum.-Comput. Stud. 59(1–2), 157–183 (2003)
Paeschke, A., Sendlmeier, W.: Prosodic characteristics of emotional speech: measurements of fundamental frequency movements. In: Speech Emotion, pp. 75–80 (2000)
Philippou-Hubner, D., Vlasenko, B., Bock, R., Wendemuth, A.: The performance of the speaking rate parameter in emotion recognition from speech. In: Proceedings of IEEE ICME, pp. 248–253 (2012)
Scherer, K.R.: Vocal affect expression: a review and a model for future research. Psychol. Bull. 99(2), 143–165 (1986)
Scherer, K.R.: How emotion is expressed in speech and singing. In: Proceedings of 1995 ICPhS, Stockholm, pp. 90–96 (1995)
Stibbard, R.: Vocal Expression of Emotions in Non-Laboratory Speech: An Investigation of the Reading/Leeds Emotion in Speech Project Annotation Data, 245 p. Linguistics and Applied Language Studies, University of Reading, Reading, RoyaumeUni (2001)
Nwe, T.L., Foo, S.W., De Silva, L.C.: Detection of stress and emotion in speech using traditional and FFT based log energy features. In: Proceedings of the 4th International Conference on Information, Communications and Signal Processing (2009)
Vlasenko, B., Prylipko, D., Philippou-Hubner, D., Wendemuth, A.: Vowels formants analysis allows straightforward detection of high arousal acted and spontaneous emotions. In: Proceedings of INTERSPEECH 2011, Florence, Italy, pp. 1577–1580 (2011)
Vogt, T., Andre, E.: Comparing feature sets for acted and spontaneous speech in view of automatic emotion recognition. In: IEEE International Conference on Multimedia and Expo, pp. 474–477 (2005)
Yüncü, E., Hacıhabiboğluy, H., Bozşahin, C.: Automatic speech emotion recognition using auditory models with binary decision tree and SVM. In: Proceedings of the 2014 22nd International Conference on Pattern Recognition, pp. 773–778. Computer Society Washington, D.C. (2014). ISBN 978-1-4799-5209-0
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Agrima, A., Elmazouzi, L., Mounir, I., Farchi, A. (2018). Detection of Negative Emotion Using Acoustic Cues and Machine Learning Algorithms in Moroccan Dialect. In: Abraham, A., Haqiq, A., Muda, A., Gandhi, N. (eds) Proceedings of the Ninth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2017). SoCPaR 2017. Advances in Intelligent Systems and Computing, vol 737. Springer, Cham. https://doi.org/10.1007/978-3-319-76357-6_10
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