Abstract
Noisy speech emotion recognition is significant in Artificial Intelligence (AI) and Human-Computer Interaction (HCI). In this paper, Compressed Sensing (CS) theory is adopted in preprocessing procedure to remove the added noise on the samples in a mandarin emotional speech corpus. A novel binary tree structure is utilized in the designing of the multi-class classifier. Acoustic features are selected to build feature subset with better emotional recognizability. The recognition accuracies and corresponding confusion matrices of the original, noisy and reconstructed speech samples are compared. The recognition performance of the reconstructed samples is better than the samples contaminated by noise and similar as the performance of original samples. The experimental results show that Compressed Sensing is feasible and effective in noisy speech emotion recognition as a preprocess method.
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
Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)
Tao, J.H., Tan, T.N.: Affective computing: a review. In: Proceedings of 1st International Conference on Affective Computing and Intelligent Interaction, vol. 10, pp. 981–995 (2005)
Ayadi, M.E., Kamel, M.S., Karray, F.: Survey on speech emotion recognition: features, classification schems, and databases. Pattern Recogn. 44(3), 572–587 (2011)
Schuller, B., Arsic, D., Wallhoff, F., Rigoll, G.: Emotion recognition in the noise applying large acoustic feature sets. Proc. Speech Prosody 5, 128 (2006)
You, M.Y., Chen, C., Bu, J.J., Liu, J., Tao, J.H.: Emotion recognition from noisy speech. Proc. ICME 7, 1653–1656 (2006)
Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)
Candès, E.J.: The restricted isometry property and its implications for compressed sensing. C.R. Math. 346(9–10), 589–592 (2008)
Sharma, P., Abrol, V., Sao, A.K.: Speech enhancement using compressed sensing. In: Proceeding of INTERSPEECH 2013, vol. 8, pp. 3274–3274 (2013)
Chapelle, O., Vapnik, V., Bousquet, O., Mukherjee, S.: Choosing multiple parameters for support vector machines. Mach. Learn. 46(1), 131–159 (2002)
Chen, S., Donoho, D.L., Saunders, M.A.: Atomic decomposition by Basis Pursuit. Siam Rev. 43(1), 129–159 (2006)
Saligrama, V., Zhao, M.: Thresholded basis pursuit: LP algorithm for order-wise optimal support recovery for sparse and approximately sparse signals from noisy random measurements. IEEE Trans. Inf. Theory 57(3), 1567–1586 (2011)
Haupt, J., Nowak, R.: Signal reconstruction from noisy random projections. IEEE Trans. Inf. Theory 52(9), 4036–4048 (2006)
Meyer, P.E., Schretter, C., Bontempi, G.: Information-theoretic feature selection in microarray dada using variable complementarity. IEEE J. Sel. Top. Sign. Proces. 2(3), 261–274 (2008)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 61501204, No. 61601198), Shandong Province Natural Science Foundation (No. ZR2015FL010), and Science and Technology Program of University of Jinan (XKY1710).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Jiang, X., He, D., Yang, X., Wang, L. (2017). Emotion Recognition from Noisy Mandarin Speech Preprocessed by Compressed Sensing. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_55
Download citation
DOI: https://doi.org/10.1007/978-3-319-63312-1_55
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63311-4
Online ISBN: 978-3-319-63312-1
eBook Packages: Computer ScienceComputer Science (R0)