Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 749604, 7 pages
http://dx.doi.org/10.1155/2014/749604
Research Article

A Research of Speech Emotion Recognition Based on Deep Belief Network and SVM

Department of Computer, Communication University of China, Beijing 100024, China

Received 27 May 2014; Revised 21 July 2014; Accepted 21 July 2014; Published 12 August 2014

Academic Editor: Stefan Balint

Copyright © 2014 Chenchen Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Z. Yongzhao and C. Peng, “Research and implementation of emotional feature extraction and recognition in speech signal,” Joural of Jiangsu University, vol. 26, no. 1, pp. 72–75, 2005. View at Google Scholar
  2. L. Zhao, C. Jiang, C. Zou, and Z. Wu, “Study on emotional feature analysis and recognition in speech,” Acta Electronica Sinica, vol. 32, no. 4, pp. 606–609, 2004. View at Google Scholar · View at Scopus
  3. H. Lee, C. Ekanadham, and A. Y. Ng, “Sparse deep belief net model for visual area V2,” in Proceedings of the 21st Annual Conference on Neural Information Processing Systems (NIPS '07), MIT Press, December 2007. View at Scopus
  4. A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 26th Annual Conference on Neural Information Processing Systems (NIPS '12), pp. 1097–1105, Lake Tahoe, Nev, USA, December 2012. View at Scopus
  5. Z. Li, “A study on emotional feature analysis and recognition in speech signal,” Journal of China Institute of Communications, vol. 21, no. 10, pp. 18–24, 2000. View at Google Scholar
  6. T. L. Nwe, S. W. Foo, and L. C. de Silva, “Speech emotion recognition using hidden Markov models,” Speech Communication, vol. 41, no. 4, pp. 603–623, 2003. View at Publisher · View at Google Scholar · View at Scopus
  7. X. Bo, “Analysis of mandarin emotional speech database and statistical prosodic features,” in Proceedings of the Interntional Conference on Affective Computing and Intelligent Interaction (ACLL '03), pp. 221–225, 2003.
  8. L. Zhao, X. Qian, C. Zhou, and Z. Wu, “Study on emotional feature derived from speech signal,” Journal of Data Acquistion & Processing, vol. 15, no. 1, pp. 120–123, 2000. View at Google Scholar
  9. G. Pengjuan and J. Dongmei, “Research on emotional speech recognition based on pitch,” Application Research of Computers, vol. 24, no. 10, pp. 101–103, 2007. View at Google Scholar
  10. P. Guo, Research of the Method of Speech Emotion Feature Extraction and the Emotion Recognition, Northwestern Polytechnical University, 2007.
  11. T. Bänziger and K. R. Scherer, “The role of intonation in emotional expressions,” Speech Communication, vol. 46, no. 3-4, pp. 252–267, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Rui and M. Zhenjiang, “Emotional speech synthesis based on PSOLA algorithm,” Journal of System Simulation, vol. 20, pp. 423–426, 2008. View at Google Scholar
  13. S. Zhijun, X. Lei, X. Yangming, and W. Zheng, “Overview of deep learning,” Application Research of Computers, vol. 29, no. 8, pp. 2806–2810, 2012. View at Google Scholar
  14. G. E. Hinton, S. Osindero, and Y. Teh, “A fast learning algorithm for deep belief nets,” Neural Computation, vol. 18, no. 7, pp. 1527–1554, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  15. Z. Sun, L. Xue, Y. Xu, and Z. Wang, “Overview of deep learning,” Application Research of Computers, vol. 29, no. 8, pp. 2806–2810, 2012. View at Google Scholar
  16. Z. Chunxia, J. Nannan, and W. Guanwei, “Introduction of restricted boltzmann machine,” China Science and Technology Papers Online, http://www.paper.edu.cn/releasepaper/content/201301-528.
  17. T. Shimmura, “Analyzing prosodic components of normal speech and emotive speech,” The Preprint of the Acoustical Society of Japan, pp. 3–18, 1995. View at Google Scholar
  18. X. Kai, J. Lei, C. Yuqiang, and X. Wei, “Deep learning: yesterday, today, and tomorrow,” Journal of Computer Research and Development, vol. 50, no. 9, pp. 1799–1804, 2013. View at Google Scholar
  19. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, “Greedy layer-wise training of deep networks,” in Advances in Neural Information Processing Systems 19, pp. 153–160, MIT Press, 2007. View at Google Scholar
  20. Y. Kim, H. Lee, and E. M. Provost, “Deep learning for robust feature generation in audio-visual emotion recognition,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '13), Vancouver, Canada, 2013.
  21. J. Zhu, X. Wu, and Z. Lv, “Speech emotion recognition algorithm based on SVM,” Computer Systems & Applications, vol. 20, no. 5, pp. 87–91, 2011. View at Google Scholar