Introducing shared-hidden-layer autoencoders for transfer learning and their application in acoustic emotion recognition

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn , Issue , 2014, Pages 4818-4822

  Deng, Jun ^a   Xia, Rui ^a,b   Zhang, Zixing ^a   Liu, Yang ^b   Schuller, Björn ^c  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF TEXAS AT DALLAS   (United States)

^c IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Cross Corpus; Emotion Recognition; Shared Hidden Layer Autoencoder; Transfer Learning

Indexed keywords

DATABASE SYSTEMS; LEARNING SYSTEMS;

ACOUSTIC EMOTION RECOGNITION; AUTO ENCODERS; BASE-LINE PERFORMANCE; CROSS-CORPUS; DOMAIN ADAPTATION; EMOTION RECOGNITION; PERFORMANCE DEGRADATION; TRANSFER LEARNING;

SIGNAL PROCESSING;

EID: 84905276961     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2014.6854517     Document Type: Conference Paper

References (29)

1. Y. Bengio, A. Courville, and P. Vincent, "Representation learning: A review and new perspectives, " Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 35, no. 8, pp. 1798-1828, 2013.
2. L. P Heck, Y. Konig, M K. Sonmez, and M. Weintraub, "Robustness to telephone handset distortion in speaker recognition by discriminative feature design, " Speech Communication, vol. 31, no. 2, pp. 181-192, 2000.
3. A. Batliner, B. Schuller, D. Seppi, S. Steidl, L. Devillers, L. Vidrascu, T. Vogt, V. Aharonson, and N. Amir, "The automatic recognition of emotions in speech, " Emotion-Oriented Systems, pp. 71-99, 2011.
4. B. Schuller, "Recognizing affect from linguistic information in 3d continuous space, " IEEE Transactions on Affective Computing, vol. 2, no. 4, pp. 192-205, October-December 2012.
5. M. El Ayadi, M. S Kamel, and F. Karray, "Survey on speech emotion recognition: Features, classification schemes, and databases, " Pattern Recognition, vol. 44, no. 3, pp. 572-587, 2011.
6. C.-N. Anagnostopoulos, T. Iliou, and I. Giannoukos, "Features and classifiers for emotion recognition from speech: A survey from 2000 to 2011, " Artificial Intelligence Review, pp. 1-23, 2012.
7. B. Schuller, B. Vlasenko, F. Eyben, M. Wollmer, A. Stuhlsatz, A. Wendemuth, and G. Rigoll, "Cross-corpus acoustic emotion recognition: Variances and strategies, " IEEE Trans. on Affective Computing, vol. 1, no. 2, pp. 119-131, 2010.
8. F. Eyben, A. Batliner, and B. Schuller, "Towards a standard set of acoustic features for the processing of emotion in speech, " in Proc. of Meetings on Acoustics. 2012, Acoustical Society of America.
9. G. Liu, Y. Lei, and J. H. Hansen, "A novel feature extraction strategy for multi-stream robust emotion identification, " in Proc. of INTERSPEECH, 2010, pp. 482-485.
10. J. Deng, Z. Zhang, E. Marchi, and B. Schuller, "Sparse autoencoder-based feature transfer learning for speech emotion recognition, " in Proc. of ACII, Geneva, Switzerland, 2013, HUMAINE Association, pp. 511-516, IEEE.
11. S. J. Pan and Q. Yang, "A survey on transfer learning, " IEEE Trans. on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345-1359, 2010.
12. T. Kanamori, S. Hido, and M. Sugiyama, "Efficient direct density ratio estimation for non-stationarity adaptation and outlier detection, " in Proc. of NIPS, 2008, pp. 809-816.
13. M. Sugiyama, S. Nakajima, H. Kashima, P. von Bunau, and M. Kawanabe, "Direct importance estimation with model selectionand its application to covariate shift adaptation, " in Proc. of NIPS, 2007, pp. 1433-1440.
14. A. Hassan, R. Damper, and M. Niranjan, "On acoustic emotion recognition: compensating for covariate shift, " Audio, Speech, and Language Processing, IEEE Transactions on, vol. 21, no. 7, pp. 1458-1468, 2013.
15. A. Gretton, A. Smola, J. Huang, M. Schmittfull, K. Borgwardt, and B. Scholkopf, "Covariate shift by kernel mean matching, " Dataset shift in machine learning, vol. 3, no. 4, pp. 5, 2009.
16. I. Goodfellow, Q. Le, A. Saxe, H. Lee, and A.Y. Ng, "Measuring invariances in deep networks, " in Proc. of NIPS, Vancouver, Canada, 2009, pp. 646-654.
17. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle, "Greedy layer-wise training of deep networks, " in Proc. of NIPS, Vancouver, Canada, 2007.
18. Y. Bengio, "Deep learning of representations for unsupervised and transfer learning, " in Proc. of ICML, Bellevue, U. S. A., 2011.
19. L. Torrey and J. Shavlik, "Transfer learning, " Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, vol. 1, pp. 242, 2009.
20. G. E Hinton and R. R Salakhutdinov, "Reducing the dimensionality of data with neural networks, " Science, vol. 313, no. 5786, pp. 504-507, 2006.
21. R. Xia and Y. Liu, "Using denoising autoencoder for emotion recognition, " in Proc. of INTERSPEECH, 2013.
22. Z. Zhang, F. Weninger, M. Wollmer, and B. Schuller, "Unsupervised Learning in Cross-Corpus Acoustic Emotion Recognition, " in Proc. of ASRU, Big Island, HY, 2011, pp. 523-528.
23. F. Eyben, A. Batliner, B. Schuller, D. Seppi, and S. Steidl, "Cross-corpus classification of realistic emotions-some pilot experiments, " in Proc. of 3rd International Workshop on EMOTION: Corpora for Research on Emotion and Affect, satellite of LREC 2010, Valletta, Malta, May 2010, ELRA, pp. 77-82, European Language Resources Association.
24. B. Schuller, S. Steidl, and A. Batliner, "The INTERSPEECH 2009 Emotion Challenge, " in Proc. of INTERSPEECH, Brisbane, U. K., 2009, pp. 2794-2797.
25. B. Schuller, D. Arsic, G. Rigoll, M. Wimmer, and B. Radig, "Audiovisual behavior modeling by combined feature spaces, " in Proc. of ICASSP, Honolulu, HY, 2007, vol. II, pp. 733-736.
26. J.H.L. Hansen and S. Bou-Ghazale, "Getting started with SUSAS: A speech under simulated and actual stress database, " in Proc. of EUROSPEECH-97, Rhodes, Greece, 1997.
27. F. Eyben, M. Wollmer, and B. Schuller, "openEAR - Introducing the Munich open-source emotion and affect recognition toolkit, " in Proc. of ACII, Amsterdam, 2009, pp. 576-581.
28. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R.Wang, and C.-J. Lin, "LIBLINEAR: A library for large linear classification, " Journal of Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
29. X. Glorot, A. Bordes, and Y. Bengio, "Domain adaptation for large-scale sentiment classification: A deep learning approach, " in Proc. of ICML, Bellevue, U. S. A., 2011