Abstract
This paper presents an analysis of the audio section of the SEMAINE database for affect detection. Chi-square and principal component analysis techniques are used to reduce the dimensionality of the audio datasets. After dimensionality reduction, different classification techniques are used to perform emotion classification at the word level. Additionally, for unbalanced training sets, class re-sampling is performed to improve the model’s classification results. Overall, the final results indicate that Support Vector Machines (SVM) performed best for all data sets. Results show promise for the SEMAINE database as an interesting corpus to study affect detection.
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References
McKeown, G., Valstar, M., Pantic, M., Cowie, R.: The SEMAINE Corpus of Emotionally Coloured Character Interactions. In: Proceedings International Conference Multimedia & Expo., pp. 1–6 (2010)
Busso, C., Lee, S., Narayanan, S.: Analysis of Emotionally Salient Aspects of Fundamental Frequency for Emotion Detection. IEEE Transactions on Audio, Speech, and Language Processing 17(4), 582–596 (2009)
Burges, C.: A Tutorial on Support Vector Machines for Pattern Recognition. Data Mining and Knowledge Discovery 2, 121–167 (1998)
Chang, C., Lin, C.: LIBSVM: A Library for Support Vector Machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Chawla, N., Bowyer, K., Hall, L., Kegelmeyer, W.: SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research 16, 341–378 (2002)
Luengo, I., Navas, E., Hernaez, I.: Feature Analysis and Evaluation for Automatic Emotion identification in Speech. IEEE Transactions on Multimedia 12(6), 490–501 (2010)
Schuller, B., Valstar, M., Eyben, F., McKeown, G., Cowie, R., Pantic, M.: AVEC 2011 – The First International Audio/Visual Emotion Challenge. In: D´Mello, S., et al. (eds.) ACII 2011, Part II, vol. 6975, pp. 415–424. Springer, Heidelberg (2011)
Witten, I., Frank, E.: Data mining: Practical Machine Learning Tools and Techniques, 2nd edn. Morgan Kaufmann Publishers Inc., San Francisco
Grimm, M., Kroschel, K., Mower, E., Narayanan, S.: Primitives based evaluation and estimation of emotions in speech. Elsevier Speech Communication 49, 787–800 (2007)
Yang, Y., Pedersen, J.: A Comparative Study on Feature Selection in Text Categorization. In: Proceedings of the 14th International Conference on Machine Learning, pp. 412–420 (1997)
El Ayadi, M., Kamel, M., Karray, F.: Survey on speech emotion recognition: Features, classification schemes, and databases. Pattern Recognition 44, 572–587 (2011)
You, M., Chen, C., Bu, J., Liu, J., Tao, J.: Emotion recognition from noisy speech. In: IEEE International Conference on Multimedia and Expo., pp. 1653–1656 (2006)
Marcano-Cedeño, A., Quintanilla-Domínguez, J., Cortina-Januchs, M.G., Andina, D.: Feature selection using Sequential Forward Selection and classification applying Artificial Metaplasticity Neural Network. In: 36th Annual Conference on IEEE Industrial Electronics Society, pp. 2845–2850 (2010)
You, M., Chen, C., Bu, J., Liu, J., Tao, J.: A hierarchical framework for speech emotion recognition. In: IEEE International Symposium on Industrial Electronics, vol. 1, pp. 515–519 (2006)
Ververidis, D., Kotropoulos, C., Pitas, I.: Automatic emotional speech classification. In: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP 2004), vol. 1, pp. I-593–I-596 (2004)
Go, H., Kwak, K., Lee, D., Chun, M.: Emotion recognition from the facial image and speech signal. In: Proceedings of the IEEE SICE 2003, vol. 3, pp. 2890–2895 (2003)
Schuller, B., Lang, M., Rigoll, G.: Robust acoustic speech emotion recognition by ensembles of classifiers. In: Proceedings of the DAGA 2005, 31, Deutsche Jahrestagung für Akustik, DEGA, pp. 329–330 (2005)
Lugger, M., Yang, B.: Combining classifiers with diverse feature sets for robust speaker independent emotion recognition. In: Proceedings of EUSIPCO (2009)
Bishop, C.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Xie, B., Chen, L., Chen, G.-C., Chen, C.: Statistical feature selection for mandarin speech emotion recognition. In: Huang, D.-S., Zhang, X.-P., Huang, G.-B. (eds.) ICIC 2005. LNCS, vol. 3644, pp. 591–600. Springer, Heidelberg (2005), doi:10.1007/11538059_62
Schuller, B., Rigoll, G., Lang, M.: Speech emotion recognition combining acoustic features and linguistic information in a hybrid support vector machine-belief network architecture. In: Proceedings of the ICASSP 2004, vol. 1, pp. 577–580 (2004)
Duda, R., Hart, P., Stork, D.: Pattern Recognition. John Wiley and Sons, Chichester (2001)
Grimm, M., Kroschel, K., Narayanan, S.: Support Vector Regression for Automatic Recognition of Spontaneous Emotions in Speech. In: Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 1085–1088 (April 2007)
Lugger, M., Yang, B.: The Relevance of Voice Quality Features in Speaker Independent emotion recognition. In: Proceeding of IEEE International Conference on Acoustics, Speech and Signal Processing, vol. 4, pp. 17–20 (April 2007)
Ververidis, D., Kotropoulos, C.: Fast sequential floating forward selection applied to emotional speech features estimated on DES and SUSAS data collections. In: Proceedings of the European Signal Processing Conference, EUSIPCO (2006)
Ververidis, D., Kotropoulos, C.: Fast and accurate sequential floating forward feature selection with the Bayes classifier applied to speech emotion recognition. Signal Processing 88(12), 2956–2970 (2008)
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Calix, R.A., Khazaeli, M.A., Javadpour, L., Knapp, G.M. (2011). Dimensionality Reduction and Classification Analysis on the Audio Section of the SEMAINE Database. In: D’Mello, S., Graesser, A., Schuller, B., Martin, JC. (eds) Affective Computing and Intelligent Interaction. ACII 2011. Lecture Notes in Computer Science, vol 6975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24571-8_43
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DOI: https://doi.org/10.1007/978-3-642-24571-8_43
Publisher Name: Springer, Berlin, Heidelberg
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