Comparison of complementary spectral features of emotional speech for German, Czech, and Slovak

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

Volumn 7403 LNCS, Issue , 2012, Pages 236-250

  Přibil, Jiří ^a   Přibilová, Anna ^b  

^a INSTITUTE OF MEASUREMENT SCIENCE   (Slovakia)

^b SLOVAK UNIVERSITY OF TECHNOLOGY   (Slovakia)

Author keywords

emotional speech; spectral features of speech; statistical analysis

Indexed keywords

EMOTIONAL SPEECH; SHANNON ENTROPY; SPECTRAL FEATURE; SPECTRAL FLATNESS; SPEECH DATABASE; SPEECH MATERIALS; STATISTICAL PARAMETERS; VOCAL-TRACTS;

COGNITIVE SYSTEMS; SPEECH ANALYSIS;

STATISTICAL METHODS;

EID: 84870318367     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-34584-5_20     Document Type: Conference Paper

References (25)

1. Chetouani, M., Mahdhaoui, A., Ringeval, F.: Time-Scale Feature Extractions for Emotional Speech Characterization. Cognitive Computation 1, 194-201 (2009)
2. Luengo, I., Navas, E., Hernáez, I.: Feature Analysis and Evaluation for Automatic Emotion Identification in Speech. IEEE Transactions on Multimedia 12, 490-501 (2010)
3. Pao, T.-L., Chen, Y.-T., Yeh, J.-H., Liao, W.-Y.: Combining Acoustic Features for Improved Emotion Recognition in Mandarin Speech. In: Tao, J., Tan, T., Picard, R.W. (eds.) ACII 2005. LNCS, vol. 3784, pp. 279-285. Springer, Heidelberg (2005)
4. Atassi, H., Riviello, M.T., Smékal, Z., Hussain, A., Esposito, A.: Emotional Vocal Expressions Recognition Using the COST 2102 Italian Database of Emotional Speech. In: Esposito, A., Campbell, N., Vogel, C., Hussain, A., Nijholt, A. (eds.) COST 2102 Int. Training School 2009. LNCS, vol. 5967, pp. 255-267. Springer, Heidelberg (2010)
5. Bozkurt, E., Erzin, E., Erdem, C.E., Erdem, A.T.: Formant Position Based Weighted Spectral Features for Emotion Recognition. Speech Communication 53, 1186-1197 (2011)
6. Iriondo, I., et al.: Automatic Refinement of an Expressive Speech Corpus Assembling Subjective Perception and Automatic Classification. Speech Communication 51, 744-758 (2009)
7. Hosseinzadeh, D., Krishnan, S.: On the Use of Complementary Spectral Features for Speaker Recognition. EURASIP Journal on Advances in Signal Processing 2008, Article ID 258184, 10 pages (2008), doi:10.1155/2008/258144
8. Berlin Database of Emotional Speech. Department of Communication Science, Institute for Speech and Communication, Technical University Berlin, http://pascal.kgw.tu-berlin.de/emodb/ (retrieved March 13, 2006)
9. Burkhardt, F., Paeschke, A., Rolfes, M., Sendlmeier, W., Weiss, B.: A Database of German Emotional Speech. In: Proc. INTERSPEECH 2005, ISCA, Lisbon, Portugal, pp. 1517-1520 (2005)
10. Přibil, J., Přibilová, A.: Application of Speaking Style Conversion in the Czech and Slovak TTS System with Cepstral Description. In: Proceedings of the 14th International Conference on Systems, Signals and Image Processing (IWSSIP 2007) & 6th EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services (EC-SIPMCS 2007), Maribor, Slovenia, pp. 289-292 (2007)
11. Přibil, J., Přibilová, A.: Spectral Flatness Analysis for Emotional Speech Synthesis and Transformation. In: Esposito, A., Vích, R. (eds.) Cross-Modal Analysis. LNCS (LNAI), vol. 5641, pp. 106-115. Springer, Heidelberg (2009)
12. Přibil, J., Přibilová, A.: Statistical Analysis of Complementary Spectral Features of Emotional Speech in Czech and Slovak. In: Habernal, I., Matoušek, V. (eds.) TSD 2011. LNCS (LNAI), vol. 6836, pp. 299-306. Springer, Heidelberg (2011)
13. Reynolds, D.A., Rose, R.C.: Robust Text-Independent Speaker Identification Using Gaussian Mixture Speaker Models. IEEE Transactions on Speech and Audio Processing 3, 72-83 (1995)
14. Hartung, J., Makambi, H.K., Arcac, D.: An Extended ANOVA F-test with Applications to the Heterogeneity Problem in Meta-Analysis. Biometrical Journal 43(2), 135-146 (2001)
15. Volaufová, J.: Statistical Methods in Biomedical Research and Measurement Science. Measurement Science Review 5(1), 1-10 (2005)
16. Vích, R.: Cepstral Speech Model, Padé Approximation, Excitation, and Gain Matching in Cepstral Speech Synthesis. In: Proceedings of the 15th Biennial EURASIP Conference Biosignal 2000, Brno, Czech Republic, pp. 77-82 (2000)
17. Li, X., Liu, H., Zheng, Y., Xu, B.: Robust Speech Endpoint Detection Based on Improved Adaptive Band-Partitioning Spectral Entropy. In: Li, K., Fei, M., Irwin, G.W., Ma, S. (eds.) LSMS 2007. LNCS, vol. 4688, pp. 36-45. Springer, Heidelberg (2007)
18. Lee, W.-S., Roh, Y.-W., Kim, D.-J., Kim, J.-H., Hong, K.-S.: Speech Emotion Recognition Using Spectral Entropy. In: Xiong, C.-H., Liu, H., Huang, Y., Xiong, Y.L. (eds.) ICIRA 2008, Part II. LNCS (LNAI), vol. 5315, pp. 45-54. Springer, Heidelberg (2008)
19. Púčik, J., Oweis, R.: CT Image Reconstruction Approaches Applied to Time-Frequency Representation of Signals. EURASIP Journal on Applied Signal Processing 2003, 422-429 (2003)
20. Kar, S., Bhagat, M., Routray, A.: EEG Signal Analysis for the Assessment and Quantification of Driver's Fatigue. Transportation Research Part F 13, 297-306 (2010)
21. Poza, J., et al.: Regional Analysis of Spontaneous MEG Rhythms in Patients with Alzheimer's Disease Using Spectral Entropy. Annals of Biomedical Engineering 36, 141-152 (2008)
22. Oppenheim, A.V., Schafer, R.W., Buck, J.R.: Discrete-Time Signal Processing, 2nd edn. Prentice-Hall (1999)
23. Boersma, P., Weenink, D.: Praat: Doing Phonetics by Computer (Version 5.2.20) [Computer Program], http://www.praat.org/ (retrieved March 25, 2011)
24. Hanzlíček, Z., Matoušek, J., Tihelka, D.: First Experiments on Text-to-Speech System Personification. In: Matoušek, V., Mautner, P. (eds.) TSD 2009. LNCS, vol. 5729, pp. 186-193. Springer, Heidelberg (2009)
25. Hanzlíček, Z.: Czech HMM-Based Speech Synthesis. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds.) TSD 2010. LNCS, vol. 6231, pp. 291-298. Springer, Heidelberg (2010)