Active learning by label uncertainty for acoustic emotion recognition

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

Volumn , Issue , 2013, Pages 2856-2860

  Zhang, Zixing ^a   Deng, Jun ^a   Marchi, Erik ^a   Schuller, Björn ^a,b  

^a TECHNICAL UNIVERSITY OF MUNICH   (Germany)

^b UNIVERSITY OF PASSAU   (Germany)

Author keywords

Active learning; Class sparseness; Confidence values; Label uncertainty

Indexed keywords

COMPUTER APPLICATIONS; COMPUTER SIMULATION;

ACOUSTIC EMOTION RECOGNITION; ACTIVE LEARNING; CLASS SPARSENESS; CONFIDENCE VALUES; CRITICAL ISSUES; HUMAN LABELLING; INFORMATIVE NESS; LARGE AMOUNTS;

ARTIFICIAL INTELLIGENCE;

EID: 84906253377     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (28)

1. B. Schuller, Z. Zhang, F. Weninger, and G. Rigoll, "Using Multiple Databases for Training in Emotion Recognition: To Unite or to Vote?" in Proc. INTERSPEECH 2011, Florence, Italy, 2011, pp. 1553-1556.
2. Z. Zhang, F. Weninger, M. Wollmer, and B. Schuller, "Unsupervised Learning in Cross-Corpus Acoustic Emotion Recognition, " in IEEE workshop on Automatic Speech Recognition and Understanding (ASRU), Big Island, HY, 2011, pp. 523-528.
3. Z. Zhang and B. Schuller, "Active Learning by Sparse Instance Tracking and Classifier Confidence in Acoustic Emotion Recognition, " in Proc. INTERSPEECH 2012, Portland, OR, 2012, 4 pages.
4. Z. Zhang, J. Deng, and B. Schuller, "Co-Training Succeeds in Computational Paralinguistics, " in Proc. 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, Canada, 2013, to appear.
5. D. Braha, Ed., Data Mining for Design and Manufacturing: Methods and Applications. Kluwer Academic, 2001.
6. B. Schuller, Z. Zhang, F. Weninger, and G. Rigoll, "Selecting Training Data for Cross-Corpus Speech Emotion Recognition: Prototypicality vs. Generalization, " in Proc. 2011 Speech Processing Conference, Tel Aviv, Israel, 2011, 4 pages.
7. Y. Freund, H. S. Seung, E. Shamir, and N. Tishby, "Selective sampling using the query by committee algorithm, " Machine learning, vol. 28, no. 2, pp. 133-168, 1997.
8. W. Chen, G. Liu, J. Guo, and Y.-J. Guo, "A new method for sample selection in active learning, " in 2009 International Conference on Machine Learning and Cybernetics, Baoding, China, 2009, pp. 2270-2274.
9. M. Li and I. Sethi, "Confidence-based active learning, " IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 28, no. 8, pp. 1251-1261, 2006.
10. B. Settles, "Active learning literature survey, " Department of Computer Sciences, University of Wisconsin-Madison, Wisconsin, WI, Tech. Rep., 2009.
11. G. Riccardi and D. Hakkani-Tur, "Active learning: Theory and applications to automatic speech recognition, " IEEE Transactions on Speech and Audio Processing, vol. 13, no. 4, pp. 504-511, 2005.
12. R. Liere, "Active learning with committees: An approach to efficient learning in text categorization using linear threshold algorithms, " Ph.D. dissertation, Oregon State Univ., Portland, 2000.
13. A. McCallum and K. Nigam, "Employing EM in pool-based active learning for text classification, " in Proc. 15th International Conference on Machine Learning (ICML), Madison, WI, 1998, pp. 359-367.
14. N. Roy and A. McCallum, "Toward optimal active learning through sampling estimation of error reduction, " in Prof. Int'l. Conf. on Machine Learning (ICML), Williamstown, MA, 2001, pp. 441-448.
15. B. Settles and M. Craven, "An analysis of active learning strategies for sequence labeling tasks, " in Proc. Empirical Methods in Natural Language Processing (EMNLP), Honolulu, HY, October 2008, pp. 1070-1079.
16. Z. Xu, R. Akella, and Y. Zhang, "Incorporating diversity and density in active learning for relevance feedback, " in Proc. European Conference on Information Retrieval (ECIR), Rome, Italy, 2007, pp. 246-257.
17. P.-H. Gosselin and M. Cord, "Active learning methods for interactive image retrieval, " IEEE Transactions on Image Processing, vol. 17, no. 7, pp. 1200-1211, 2008.
18. S. Sivaraman and M. Trivedi, "A general active-learning framework for on-road vehicle recognition and tracking, " IEEE Transactions on Intelligent Transportation Systems, vol. 11, no. 2, pp. 267-276, 2010.
19. I. Sneddon, M. McRorie, G. McKeown, and J. Hanratty, "The belfast induced natural emotion database, " IEEE Transactions on Affective Computing, vol. 3, no. 1, pp. 32-41, 2012.
20. B. Schuller, "Multimodal Affect Databases - Collection, Challenges & Chances, " in Handbook of Affective Computing, R. A. Calvo, S. DMello, J. Gratch, and A. Kappas, Eds. Oxford, UK: Oxford University Press, 2013.
21. D. Seppi, A. Batliner, B. Schuller, S. Steidl, T. Vogt, J. Wagner, L. Devillers, L. Vidrascu, N. Amir, and V. Aharonson, "Patterns, Prototypes, Performance: Classifying Emotional User States, " in Proc. INTERSPEECH 2008, Brisbane, Australia, 2008, pp. 601- 604.
22. M. Grimm and K. Kroschel, "Evaluation of natural emotions using self assessment manikins, " in 2005 IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Cancun, Mexico, 2005, pp. 381-385.
23. S. G. Karadogan and J. Larsen, "Combining semantics and acoustic features for valence and arousal recognition of speech, " in Proc. 3rd International Workshop on Cognitive Information Processing, Baiona, Spain, 2012, no pagination.
24. S. Steidl, Automatic Classification of Emotion-Related User States in Spontaneous Children's Speech. Berlin: Logos Verlag, 2009.
25. B. Schuller, S. Steidl, and A. Batliner, "The INTERSPEECH 2009 Emotion Challenge, " in Proc. INTERSPEECH 2009, Brighton, UK, 2009, pp. 312-315.
26. F. Eyben, M. Wollmer, and B. Schuller, "open SMILE - The Munich Versatile and Fast Open-Source Audio Feature Extractor, " in Proc. ACM Multimedia (MM), Florence, Italy, 2010, pp. 1459- 1462.
27. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten, "The WEKA data mining software: An update, " ACM SIGKDD Explorations Newsletter, vol. 11, no. 1, pp. 10-18, 2009.
28. B. Schuller, A. Batliner, S. Steidl, and D. Seppi, "Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge, " Speech Communication, vol. 53, no. 9, pp. 1062-1087, 2011.