Abstract
Hierarchical systems are powerful tools to deal with non-linear data with a high variability. We show in this paper that regressing a bounded variable on such data is a challenging task. As an alternate, we propose here a two-step process. First, an ensemble of ordinal classifiers affect the observation to a given range of the variable to predict and a discrete estimate of the variable. Then, a regressor is trained locally on this range and its neighbors and provides a finer continuous estimate. Experiments on affect audio data from the AVEC’2014 and AV+EC’2015 challenges show that this cascading process can be compared favorably to the state of the art and challengers results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bitouk, D., Verma, R., Nenkova, A.: Class-level spectral features for emotion recognition. Speech Commun. 52(7), 613–625 (2010)
Chang, J., Scherer, S.: Learning representations of emotional speech with deep convolutional generative adversarial networks. In: ICASSP, pp. 2746–2750, 2017
Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, P.W.: SMOTE: synthetic minority oversampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)
Drucker, H., Burges, C., Kaufman, L., Smola, A.J., Vapnik, V.: Support vector regression machines. In: Advances in Neural Information Processing Systems, pp. 155–161 (1997)
Ekman, P.: Basic emotions. In: Handbook of Cognition and Emotion, pp. 45–60. Wiley, New York (1999)
Frank, E., Hall, M.: A simple approach to ordinal classification. In: De Raedt, L., Flach, P. (eds.) ECML 2001. LNCS (LNAI), vol. 2167, pp. 145–156. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44795-4_13
Fontaine, J.R., Scherer, K.R., Roesch, E.B., Ellsworth, P.C.: The world of emotions is not two-dimensional. Psychol. Sci. 18(12), 1050–1057 (2007)
Grandjean, D., Sander, D., Scherer, K.R.: Conscious emotional experience emerges as a function of multilevel, appraisal-driven response synchronization. Conscious. Cogn. 17(2), 484–495 (2008)
Guo, G., Fu, Y., Wang, T.S., Dyer, C.R.: Locally adjusted robust regression for human age estimation. In: WACV (2008)
Han, J., Zhang, Z., Ringeval, F., Schuller, B.: Prediction-based learning for continuous emotion recognition in speech. In: ICASSP, pp. 5005–5009 (2017)
He, L., Jiang, D., Yang, L., Pei, E., Hu, P., Sahli, H.: Multimodal affective dimension prediction using deep bidirectional long short-term memory recurrent neural networks. In: AVEC, pp. 73–80 (2015)
Hinton, G.E., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Nicolaou, M.A., Gunes, H., Pantic, M.: Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space. IEEE Trans. Affect. Comput. 2(2), 92–105 (2011)
Noroozi, F., Sapinski, T., Kaminska, D., Anbarjafari, G.: Vocal-based emotion recognition using random forests and decision tree. Int. J. Speech Technol. 20(2), 239–246 (2017)
Qiao, X.: Noncrossing ordinal classification. arXiv:1505.03442 (2015)
Ringeval, F., et al.: AV+EC 2015: the first affect recognition challenge bridging across audio, video, and physiological data. In: AVEC, pp. 3–8 (2015)
Russell, J.: A circumplex model of affect. J. Pers. Soc. Psychol. 39(6), 1161–1178 (1980)
Sethu, V., Ambikairajah, E., Epps, J.: Empirical mode decomposition based weighted frequency feature for speech-based emotion classification. In: ICASSP, pp. 5017–5020 (2008)
Thukral, P., Mitra, K., Chellappa, R.: A hierarchical approach for human age estimation. In: ICASSP, pp. 1529–1532 (2012)
Valstar, M.F., et al.: AVEC 2014: 3D dimensional affect and depression recognition challenge. In: AVEC (2014)
Acknowledgment
This work has been partially supported by the French National Agency (ANR) in the frame of its FRQC program (TEEC, project number ANR-16-FRQC-0009-03).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Sazadaly, M., Pinchon, P., Fagot, A., Prevost, L., Bertrand, M.M. (2018). Fast and Accurate Affect Prediction Using a Hierarchy of Random Forests. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_75
Download citation
DOI: https://doi.org/10.1007/978-3-030-01418-6_75
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-01417-9
Online ISBN: 978-3-030-01418-6
eBook Packages: Computer ScienceComputer Science (R0)