Abstract
The identification of emotion is a challenging task due to the rapid development of human–computer interaction framework. Speech Emotion Recognition (SER) can be characterized as the extraction of the emotional condition of the narrator from their spoken utterances. The detection of emotion is troublesome to the computer since it differs according to the speaker. To solve this setback, the system is implemented based on Adaptive Fractional Deep Belief Network (AFDBN) and Reinforcement Learning (RL). Pitch chroma, spectral flux, tonal power ratio and MFCC features are extracted from the speech signal to achieve the desired task. The extracted feature is then given into the classification task. Finally, the performance is analyzed by the evaluation metrics which is compared with the existing systems.
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
References
Mencattini, A., Martinelli, E., Costantini, G., Todisco, M., Basile, B., Bozzali, M., Di Natale, C.: Speech emotion recognition using amplitude modulation parameters. Knowl.-Based Syst. 63, 68–81 (2014)
Omar, M.K.: A factor analysis model of sequences for language recognition. In: Spoken Language Technology Workshop (SLT), pp. 341–347. IEEE, California (2016)
Lu, C.-X., Sun, Z.-Y., Shi, Z.-Z., Cao, B.-X.: Using emotions as intrinsic motivation to accelerate classic reinforcement learning. In: International Conference on Information System and Artificial Intelligence (ISAI), pp. 332–337. IEEE, China (2016)
Newland, E.J., Xu, S., Miranker, W.L.: A neural network-based approach to modeling the allocation of behaviors in concurrent schedule, variable interval learning. In: Fourth International Conference on Natural Computation, ICNC’08, vol. 2, pp. 245–249. IEEE, China (2008)
Wang, K., An, N., Li, B.N., Zhang, Y., Li, L.: Speech emotion recognition using Fourier parameters. IEEE Trans. Affect. Comput. 6(1), 69–75 (2015)
Jang, E.-H., Park, B.-J., Kim, S.-H., Chung, M.-A., Park, M.-S., Sohn, J.-H.: Emotion classification based on bio-signals emotion recognition using machine learning algorithms. In: International Conference on Information Science, Electronics and Electrical Engineering (ISEEE), vol. 3, pp. 1373–1376. IEEE, Japan (2014)
Hinton, G.E., Osindero, S., Teh, Y.-W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527–1554 (2006)
Ghahabi, O., Hernando, J.: Deep learning backend for single and multisession i-vector speaker recognition. J. IEEE/ACM Trans. Audio Speech Lang. Process. 25(4), 807–817 (2017)
Cruz, F., Twiefel, J., Magg, S., Weber, C., Wermter, S.: Interactive reinforcement learning through speech guidance in a domestic scenario. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1341–1348, Killarney, Ireland (2015)
Kim, E.H., Hyun, K.H., Kim, S.H., Kwak, Y.K.: Improved emotion recognition with a novel speaker-independent feature. IEEE/ASME Trans. Mechatron. 14(3), 317–325 (2009)
Mao, Q., Dong, M., Huang, Z., Zhan, Y.: Learning salient features for speech emotion recognition using convolutional neural networks. IEEE Trans. Multimedia 16(8), 2203–2213 (2014)
Hoque, S., Salauddin, F., Rahman, A.: Neighbour cell list optimization based on cooperative q-learning and reinforced back-propagation technique. In: Radio Science Meeting (Joint with AP-S Symposium), 2015 USNC-URSI, pp. 215–215. IEEE, Canada (2015)
Gharsellaoui, S., Selouani, S.-A., Dahmane, A.O.: Automatic emotion recognition using auditory and prosodic indicative features. In: 2015 IEEE 28th Canadian Conference on, Electrical and Computer Engineering (CCECE), pp. 1265–1270. IEEE, Canada (2015)
Lerch, A.: An Introduction to Audio Content Analysis: Applications in Signal Processing and Music Informatics, p. 272. Wiley IEEE Press, July 2012
Peeters, G.: Chroma-based estimation of musical key from audio-signal analysis. In: Proceedings of the 7th International Conference on Music Information Retrieval. Victoria (BC), Canada (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Sangeetha, J., Jayasankar, T. (2019). Emotion Speech Recognition Based on Adaptive Fractional Deep Belief Network and Reinforcement Learning. In: Mallick, P., Balas, V., Bhoi, A., Zobaa, A. (eds) Cognitive Informatics and Soft Computing. Advances in Intelligent Systems and Computing, vol 768. Springer, Singapore. https://doi.org/10.1007/978-981-13-0617-4_16
Download citation
DOI: https://doi.org/10.1007/978-981-13-0617-4_16
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-0616-7
Online ISBN: 978-981-13-0617-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)