High-Order Markov Random Fields and Their Applications in Cross-Language Speech Recognition

1. Nwe, T. L., S. W. Foo, L. C. De Silva. Speech Emotion Recognition Using Hidden Markov Models. - Speech Communication, Vol. 41, 2003, No 4, pp. 603-623.10.1016/S0167-6393(03)00099-2Search in Google Scholar

2. Steidl, S. Automatic Classification of Emotion-Related User States in Spontaneous Children's Speech. Ph. D. Thesis, FAU Erlangen-Nuremberg, Logos Verlag, Berlin Germany, 2012.Search in Google Scholar

3. Huang, C., Y. Zhao, Y. Jin, Y. Yu, L. Zhao. A Study on Feature Analysis and Recognition for Practical Speech Emotion. - Journal of Electronics & Information Technology, Vol. 33, 2011, No 1, pp. 112-116.10.3724/SP.J.1146.2009.00886Search in Google Scholar

4. Zou, C., C. Huang, D. Han, L. Zhao. Detecting Practical Speech Emotion in a Cognitive Task. - In: Proc. of 20th Computer Communications and Networks, Maui, HI, USA, 2011.10.1109/ICCCN.2011.6005883Search in Google Scholar

5. Wu, S., T. H. Falk, W. Y. Chan. Automatic Speech Emotion Recognition Using Modulation Spectral Features. - Speech Communication, Vol. 53, 2011, pp. 768-785.10.1016/j.specom.2010.08.013Search in Google Scholar

6. Ferreiros, J., J. M. Pardo. Improving Continuous Speech Recognition in Spanish by Phone- Class Semi-Continuous HMMs with Pausing and Multiple Pronunciations. - Speech Communication, Vol. 29, 1999, No 1, pp. 65-76.10.1016/S0167-6393(99)00013-8Search in Google Scholar

7. Mohamed, A., K. N. Nair. HMM/ANN Hybrid Model for Continuous Malayalam Speech Recognition. - Procedia Engineering, Vol. 30, 2012, pp. 616-622.10.1016/j.proeng.2012.01.906Search in Google Scholar

8. You, C. H., K. A. Lee, H. L i. GMM-SVM Kernel with a Bhattacharyya-Based Distance for Speaker Recognition. - IEEE Transactions on Audio, Speech, and Language Processing, Vol. 18, 2010, No 6, pp. 1300-1312.10.1109/TASL.2009.2032950Search in Google Scholar

9. Wu, C. H., Y. H. Chiu, C. J. Shia. Automatic Segmentation and Identification of Mixed- Language Speech Using Delta-BIC and LSA-Based GMMs. - IEEE Transactions on Audio, Speech, and Language Processing, Vol. 14, 2006, No 1, pp. 266-276.10.1109/TSA.2005.852992Search in Google Scholar

10. Kockmann, M., L. Burget, J. H. Cernocky. Application of Speaker and Language Identification State-of-the-Art Techniques for Emotion Recognition. - Speech Communication, Vol. 53, 2011, No 9, pp. 1172-1185.10.1016/j.specom.2011.01.007Search in Google Scholar

11. Yeh, J.-H., T.-L. Pao, C.-Y. Lin, Y.-W. Tsai, Y.-T. Chen. Segment-Based Emotion Recognition from Continuous Mandarin Chinese Speech. - Computers in Human Behavior, 2011, No 27, pp. 1545-1552.10.1016/j.chb.2010.10.027Search in Google Scholar

12. Huang, C., B. A. Efraty, U. Kurkure. Facial Landmark Configuration for Improved Detection. - In: Proc. of IEEE International Workshop on Information Forensics and Security, Tenerife, Spain, 2012, pp. 13-18.10.1109/WIFS.2012.6412618Search in Google Scholar

13. Barrett, L. F. Discrete Emotions or Dimensions, the Role of Valence Focus and Arousal Focus - Cognition & Emotion, Vol. 12, 1998, No 4, pp. 579-599.10.1080/026999398379574Search in Google Scholar

14. Zhang, X., C. Huang, L. Zhao, C. Zou. Recognition of Practical Speech Emotion Using Improved Shuffled Frog Leaping Algorithm. - Acta Acustica, Vol. 39, 2014, No 2, pp. 271-280.Search in Google Scholar

15. Ishikawa, H. Transformation of General Binary MRF Minimization to the First-Order Case. - IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, No 33, pp. 1234-1249.10.1109/TPAMI.2010.9120421673Search in Google Scholar

16. Rother, C., V. Kolmogorov, V. Lempitsky, M. Szummer. Optimizing Binary MRFs via Extended Roof Duality. - In: Proc. of IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, 2007, pp. 1-8.10.1109/CVPR.2007.383203Search in Google Scholar

17. Abend, K., T. J. Harley, L. N. Kanal. Classification of Binary Random Patterns. - IEEE Transactions on Information Theory, Vol. 11, 1965, pp. 538-544.10.1109/TIT.1965.1053827Search in Google Scholar

18. Geman, S., D. Geman. Stochastic Relaxation Gibbs Distribution and the Bayesian Restoration of Images. - IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 16,1984, pp. 721-741. 10.1109/TPAMI.1984.4767596Search in Google Scholar