Analyzing the memory of BLSTM neural networks for enhanced emotion classification in dyadic spoken interactions

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

Volumn , Issue , 2012, Pages 4157-4160

  Wöllmer, Martin ^a   Metallinou, Angeliki ^b   Katsamanis, Nassos ^b   Schuller, Björn ^a   Narayanan, Shrikanth ^b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

Author keywords

context modeling; emotion recognition; Long Short Term Memory; sequential Jacobian

Indexed keywords

BI-DIRECTIONAL CONTEXTS; CONTEXT MODELING; CONTEXTUAL INFORMATION; EMOTION CLASSIFICATION; EMOTION RECOGNITION; JACOBIANS; MEMORY BLOCKS; SHORT TERM MEMORY; SPOKEN INTERACTION;

BRAIN; FEEDFORWARD NEURAL NETWORKS; RECURRENT NEURAL NETWORKS; SIGNAL PROCESSING;

PSYCHOLOGY COMPUTING;

EID: 84867614588     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2012.6288834     Document Type: Conference Paper

References (14)

1. R. Cowie, E. Douglas-Cowie, N. Tsapatsoulis, G. Votsis, S. Kollias, W. Fellenz, and J. G. Taylor, "Emotion recognition in human-computer interaction," IEEE Signal Processing Magazine, vol. 18, no. 1, pp. 32-80, 2001.
2. M. Black, A. Katsamanis, C.-C. Lee, A. Lammert, B. Baucom, A. Christensen, P. G. Georgiou, and S. S. Narayanan, "Automatic classification of married couples' behavior using audio features," in Proc. of Interspeech, 2010, pp. 2030-2033.
3. L. F. Barrett and E. A. Kensinger, "Context is routinely encoded during emotion perception," Psychological Science, vol. 21, pp. 595-599, 2010.
4. C.-C. Lee, C. Busso, S. Lee, and S. Narayanan, "Modeling mutual influence of interlocutor emotion states in dyadic spoken interactions," in Proc. of Interspeech, 2009, pp. 1983-1986.
5. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997.
6. A. Graves, Supervised sequence labelling with recurrent neural networks, Ph.D. thesis, Technische Universität München, 2008.
7. M. Wöllmer, B. Schuller, F. Eyben, and G. Rigoll, "Combining long short-term memory and dynamic bayesian networks for incremental emotion-sensitive artificial listening," IEEE Journal of Selected Topics in Signal Processing, vol. 4, no. 5, pp. 867-881, 2010.
8. M. Wöllmer, A. Metallinou, F. Eyben, B. Schuller, and S. Narayanan, "Context-sensitive multimodal emotion recognition from speech and facial expression using bidirectional lstm modeling," in Proc. of Interspeech, 2010, pp. 2362-2365.
9. M. A. Nicolaou, H. Gunes, and M. Pantic, "Continuous prediction of spontaneous affect from multiple cues and modalities in valence-arousal space," IEEE Transactions on Affective Computing, vol. 2, pp. 92-105, 2011.
10. C. Busso, M. Bulut, C-C Lee, A.Kazemzadeh, E. Mower, S. Kim, J. Chang, S.Lee, and S.Narayanan, "IEMOCAP: interactive emotional dyadic motion capture database," Language Resources and Evaluation, vol. 42, pp. 335-359, 2008.
11. M. Schuster and K. K. Paliwal, "Bidirectional recurrent neural networks," IEEE Transactions on Signal Processing, vol. 45, pp. 2673-2681, 1997.
12. I. Cohen, Q. T. Xiang, S. Zhou, X. Sean, Z. Thomas, and T. S. Huang, "Feature selection using principal feature analysis," 2002.
13. A. Metallinou, C.Busso, S. Lee, and S. Narayanan, "Visual emotion recognition using compact facial representations and viseme information," in Proc. of ICASSP, 2010, pp. 2474-2477.
14. F. Eyben, M. Wöllmer, and B. Schuller, "openSMILE - the Munich versatile and fast open-source audio feature extractor," in Proc. of ACM Multimedia, 2010, pp. 1459-1462.