Acoustic emotion recognition using dynamic bayesian networks and multi-space distributions

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

Volumn , Issue , 2009, Pages 336-339

  Barra Chicote, R ^a   Fernandez, F ^a   Lutfi, S ^a   Lucas Cuesta, J M ^a   Macias Guarasa, J ^b   Montero, J M ^a   San Segundo, R ^a   Pardo, J M ^a  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

Automatic emotion recognition; Dynamic bayesian networks; Emotion challenge; Multi space probability distribution

Indexed keywords

ACOUSTIC EMOTION RECOGNITION; DYNAMIC BAYESIAN NETWORK; DYNAMIC BAYESIAN NETWORKS; EMOTION RECOGNITION; EMOTIONAL SPEECH; EXPERIMENTAL EVALUATION; SPACE DISTRIBUTION; SPEECH TECHNOLOGY; TEST SETS;

ACOUSTICS; DISTRIBUTED PARAMETER NETWORKS; FACE RECOGNITION; FLUORINE CONTAINING POLYMERS; INFERENCE ENGINES; INTELLIGENT NETWORKS; PROBABILITY DISTRIBUTIONS; SPEECH COMMUNICATION; SPEECH RECOGNITION;

BAYESIAN NETWORKS;

EID: 70450192757     PISSN: None     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Schuller, B., Steidl, S. and Batliner, A. "The Interspeech 2009 Emotion Challenge", Interspeech (2009), ISCA, Brighton, UK, 2009.
2. Barra-Chicote, R., Montero, J.M, Macias-Guarasa J., D'haro, L.F., San-Segundo, R., Cordoba, R. "Prosodic and segmental rubrics in emotion identification". In Proc. of International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1085-1088, May 2006.
3. Barra-Chicote, R., Montero, J.M., Macias-Guarasa J., Lufti, S., Lucas, J.M., Fernandez, F., D'haro, L.F., San-Segundo, R., Ferreiros, J., Cordoba, R., and Pardo J.M.. "Spanish Expressive Voices: Corpus for Emotion Research in Spanish". In Proc. of 6th International Conference on Language Resources and Evaluation (LREC2008), May 2008.
4. Lutfi S.L., Montero, J.M., Barra-Chicote, R., Lucas-Cuesta, J.M. and Gallardo-Antoln, A. "Expressive speech identifications based on Hidden Markov Model". Proceedings of the International Conference on Health Informatics (HEALTHINF), pp. 488-494. Porto (Portugal). January 14-17 2009.
5. Barra-Chicote, R, Macias-Guarasa, J., Montero, J.M., Rincon, C., Fernandez, F. and Cordoba. R. "In Search of Primary Rubrics for Language Independent Emotional Speech Identification". In Proc. of IEEE Workshop on Intelligent Speech Processing, October 2007.
6. Young, S.J., Evermann, G., Gales, M.J.F., Kershaw, D., Moore, G., Odell, J.J., Ollason, D.G., Povey, D., Valtchev, V. and Woodland, P.C. "The HTK book version 3.4 Manual". Cambridge University Engineering Department, Cambridge, UK.
7. The HTS working group, "HMM-based Speech Synthesis System (HTS), http://hts.sp.nitech.ac.jp". Last access: April 2008.
8. Gales, Mark and Young, Steve "The application of hidden Markov models in speech recognition". Foundations and Trends in Signal Processing Vol. 1, No. 3 (2007) 195304
9. Murphy K.P. "Dynamic Bayesian Networks: Representation, Inference and Learning" PhD. Thesis. California University, Berkeley, 2002.
10. Tokuda, K., Masuko, T., Miyazaki N. and Kobayashi T. "Multi-Space Probability Distribution HMM", IECI TRANS. INF. & SYSTEMS, vol. E85-D, n 3, March 2002.
11. Bilmes, Jeff. "http://ssli.ee.washington.edu/ bilmes/gmtk". Last access: April 2009.
12. Steidl, S. "Automatic Classification of Emotion-Related User States in Spontaneous Children's Speech", Logos Verlag, Berlin, 2009.