Active learning for dimensional speech emotion recognition

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2013, Pages 2841-2845

  Han, Wenjing ^a,c   Li, Haifeng ^a   Ruan, Huabin ^b   Ma, Lin ^a   Sun, Jiayin ^a   Schuller, Björn ^a,c,d  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b TSINGHUA UNIVERSITY   (China)

^c TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^d IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

Active learning; Affective computing; Continuous emotion representation; Speech emotion recognition

Indexed keywords

HUMAN COMPUTER INTERACTION;

ACTIVE LEARNING; AFFECTIVE COMPUTING; CONFIDENCE SCORE; EMOTION REPRESENTATION; EMOTIONAL DIMENSIONS; MEASURE FUNCTION; SPEECH EMOTION RECOGNITION; SPEECH EMOTION RECOGNITION SYSTEMS;

ARTIFICIAL INTELLIGENCE;

EID: 84906247300     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. M. Grimm, K. Kroschel, and S. Narayanan, "Support vector regression for automatic recognition of spontaneous emotions in speech, " in Proc. ICASSP, vol. IV, Honolulu, Hawaii, USA, 2007, pp. 1085-1088.
2. M.Wöllmer, F. Eyben, S. Reiter, B. Schuller, C. Cox, E. Douglas- Cowie, and R. Cowie, "Abandoning emotion classes - Towards continuous emotion recognition with modelling of long-range dependencies, " in Proc. INTERSPEECH, Brisbane, Australia, 2008, pp. 597-600.
3. T. Giannakopoulos, A. Pikrakis, and S. Theodoridis, "A dimensional approach to emotion recognition of speech from movies, " in Proc. ICASSP, Taipei, Taiwan, 2009, pp. 65-68.
4. F. Eyben, M.Wöllmer, A. Graves, B. Schuller, E. Douglas-Cowie, and R. Cowie, "On-line emotion recognition in a 3-D activationvalence- Time continuum using acoustic and linguistic cues, " Journal on Multimodal User Interfaces, vol. 3, pp. 7-19, 2010.
5. H. Gunes and B. Schuller, "Categorical and dimensional affect analysis in continuous input: Current trends and future directions, " Image and Vision Computing, July 2012.
6. R. Cowie, E. Douglas-Cowie, S. Savvidou, E. McMahon, S. M., and M. Schröder, "FEELTRACE: An instrument for recording perceived emotion in realtime, " in Proc. ISCA Workshop on Speech and Emotion: Developing a Conceptual Framework, Northern, Ireland, 2000, pp. 19-24, .
7. R. Cowie, C. Cox, J.-C. Martin, A. Batliner, D. Heylen, and K. Karpouzis, "Issues in Data Labelling, " in Emotion-Oriented Systems: The Humaine Handbook, P. P., C. Pelachaud, and R. Cowie, Eds. Berlin Heidelberg: Springer, 2011, pp. 215-244.
8. M. Grimm and K. Kroschel, "Evaluation of natural emotions using self assessment manikins, " in Proc. ASRU, Cancun, Mexico, 2005, pp. 381-385.
9. G. McKeown, M. Valstar, R. Cowie, and M. Pantic, "The SEMAINE corpus of emotionally coloured character interactions, " in Proc. ICME, Singapore, 2010, pp. 1079-1084.
10. D. J. C. MacKay, "Information-based objective functions for active data selection, " Neural Computation, vol. 4, pp. 589-603, 1992.
11. B. Settles, "Active learning literature survey, " University of Wisconsin, Madison, Tech. Rep. 1648, 2010.
12. D. Hakkani-Tur, G. Tur, M. Rahim, and G. Riccardi, "Unsupervised and active learning in automatic speech recognition for call classification, " in Proc. ICASSP, Montreal, Canada, 2004, pp. 429-432.
13. G. Riccardi and D. Hakkani-Tür, "Active learning: Theory and applications to automatic speech recognition, " IEEE Transactions on Speech and Audio Processing, vol. 13, no. 4, pp. 1-8, 2005.
14. B. Settles, M. Craven, and S. Ray, "Multiple instance active learning, " in NIPS, vol. 20, Vancouver, B.C., Canada, 2008, pp. 1289- 1296.
15. Z. Zhang and B. Schuller, "Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition, " in Proc. INTERSPEECH 2012. Portland, OR, USA: ISCA, 2012, 4 pages.
16. A. Bondu and V. Lemaire, "A multi-criterion active learning strategy: Application to emotion detection in speech, " in Conf́erence francophone sur lapprentissage automatique (CAP), Ile de Porquerolle, France, 2008.
17. D. Wu and T. Parsons, "Active class selection for arousal classification, " in Proc. Affective Computing and Intelligent Interaction (ACII), Memphis, TN, USA, October 2011, pp. 132-141.
18. D.Wu, C. Courtney, B. Lance, S. Narayanan, M. Dawson, K. Oie, and T. Parsons, "Optimal arousal identification and classification for affective computing using physiological signals: Virtual Reality Stroop task, " IEEE Transactions on Affective Computing, vol. 1, no. 2, pp. 109-118, july-dec. 2010.
19. B. Schuller, S. Steidl, and A. Batliner, "The interspeech 2009 emotion challenge, " in Proc. INTERSPEECH. Brighton, U.K.: ISCA, 2009, pp. 312-315.
20. D. Su and P. Fung, "Personalized music emotion classification via active learning, " in Proc. 2nd international ACM workshop on Music information retrieval with user-centered and multimodal strategies (MIRUM), Nara, Japan, 2012, pp. 57-62.
21. H. Gunes, B. Schuller, M. Pantic, and R. Cowie, "Emotion representation, analysis and synthesis in continuous space: A survey, " in Proc. IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG), 2011, pp. 827-834.
22. B. Schuller, M. Valstar, F. Eyben, R. Cowie, and M. Pantic, "AVEC 2012 - The Continuous Audio/Visual Emotion Challenge, " in Proc. 14th ACM International Conference on Multimodal Interaction (ICMI), Santa Monica, CA, 2012, pp. 449-456.
23. F. Eyben, M. Wöllmer, and B. Schuller, "openSMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor, " in Proc. ACM Multimedia, Florence, Italy, 2010, pp. 1459-1462.
24. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: An update, " SIGKDD Explorations, vol. 11, no. 1, 2009.
25. J. Deng and B. Schuller, "Confidence Measures in Speech Emotion Recognition Based on Semi-supervised Learning, " in Proc. INTERSPEECH 2012. Portland, OR, USA: ISCA, 2012, 4 pages.
26. J. Deng, W. Han, and B. Schuller, "Confidence Measures for Speech Emotion Recognition: A Start, " in Proc. 10th ITG Conference on Speech Communication. Braunschweig, Germany: IEEE, 2012, pp. 1-4.
27. M. Schröder, Speech and Emotion Research. An Overview of Research Frameworks and a Dimensional Approach to Emotional Speech Synthesis, ser. Reports in Phonetics, University of the Saarland. Institute for Phonetics, University of Saarbrücken, 2004, vol. 7.
28. D. Wu, B. J. Lance, S. S. Narayanan, and T. D.Parsons, "Transfer learning and active class selection for arousal classification: Learning an optimal classifier from a minimum number of userspecific training examples, " IEEE Transactions on Affective Computing, 2012.