Investigating the use of formant based features for detection of affective dimensions in speech

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6975 LNCS, Issue PART 2, 2011, Pages 369-377

  Kim, Jonathan C ^a   Rao, Hrishikesh ^a   Clements, Mark A ^a  

^a GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

emotion; feature selection; formants

Indexed keywords

CLASSIFICATION OF EMOTIONS; CLASSIFICATION SYSTEM; EMOTION; FORMANTS; GAUSSIAN MIXTURE MODEL; MAXIMAL RELEVANCE; SPECTRAL FEATURE; GAUSSIAN MIXTURE MODEL CLASSIFIERS; MINIMAL REDUNDANCY; SELECTION CRITERIA;

INTELLIGENT COMPUTING; FEATURE EXTRACTION; GAUSSIAN DISTRIBUTION;

FEATURE EXTRACTION; CLASSIFICATION (OF INFORMATION);

EID: 80054842597     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24571-8_48     Document Type: Conference Paper

References (11)

1. Goodluck, H.: Language acquisition: A linguistic introduction, p. 162. Wiley- Blackwell (1991)
2. Petrushin, V.: Emotion in speech: Recognition and application to call centers. In: Artificial Neu. Net. In Engr. (ANNIE 1999), pp. 7-10 (1999)
3. McKeown, G., Valstar, M.F., Cowie, R., Pantic, M.: The SEMAINE corpus of emotionally coloured character interactions. In: IEEE International Conference on Multimedia and Expo. (ICME), pp. 1079-1084 (2010)
4. Schuller, B., Valstar, M., Eyben, F., McKeown, G., Cowie, R., Pantic, M.: AVEC 2011 - The First International Audio/Visual Emotion Challenge. In: D'Mello, S., et al. (eds.) ACII 2011, Part II, vol. 6975, pp. 415-424. Springer, Heidelberg (2011)
5. Bresch, E., Kim, Y.C., Nayak, K., Byrd, D., Narayanan, S.: Seeing speech: Capturing vocal tract shaping using real-time magnetic resonance imaging. IEEE Signal Processing Magazine, 123-132 (2008)
6. Ververidis, D., Kotropoulos, C.: Emotional speech recognition: Resources, features, and methods. Speech Communication 48(9), 1162-1181 (2006)
7. Liscombe, J., Venditti, J., Hirschberg, J.: Classifying subject ratings of emotional speech using acoustic features. In: EUROSPEECH-2003, pp. 725-728 (2003)
8. Oppenheim, A.V., Schafer, R.W., Buck, J.R., et al.: Discrete-time signal processing. Prentice hall, Englewood Cliffs (1989, 1999)
9. Peng, H., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1226-1238 (2005)
10. Ding, C., Peng, H.: Minimum Redundancy Feature Selection from Microarray Gene Expression Data. Journal of Bioinformatics and Computational Biology 3(2), 185-205 (2005)
11. Ruiz, R., Aguilar, J.S., Riquelme, J.: Best agglomerative ranked subset for feature selection. In: JMLR Workshop and Conference Proceedings. New Challenges for Feature Selection in Data Mining and Knowledge Discovery, vol. 4, pp. 148-162 (2008)