To date, study on emotion recognition has focused on detecting the values of pitch, formant, or cepstrum from the variation of speech according to changing emotions. However, the values of emotional speech features vary by not only emotions but also speakers. Because each speaker has unique frequency characteristics, it is difficult to apply the same manner to different speakers. Therefore, in the present work we considered the personal characteristics of speech. To this end, we analyzed the frequency characteristics for a user and chose the frequency ranges that are sensitive to variation of emotion. From these results, we designed a personal filter bank and extracted emotional speech features using this filter bank. This method showed about 90% recognition rate although there are differences among individuals.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ang J, Dhillon R, Krupski A, Shriberg E, Stolcke A (2002) Prosody-based automatic detection of annoyance and frustration in human-computer dialog. In: Hansen JHL, Pellom B (eds) International Conference on Spoken Language Processing, ISCA Archive, pp 2037-2040
Bezooijen Rv (1984) The Characteristics and Recognizability of Vocal Expression of Emotions. Walter de Gruyter, Inc., The Netherlands
Cowie R, Douglas-Cowie E, Tsapatsoulis N, Votsis G, Kollias S, Fellenz W, Taylor JG (2001) Emotion recognition in human-computer interaction. In: Chang S-F, Schneiderman S, (eds) IEEE Signal Processing Magazine, IEEE Signal Processing Society, pp 32-80
Esau N, Kleinjohann B, Kleinjohann L, Stichling D (2003) MEXI: Machine with Emotionally eXtended Intelligence. In: Abraham A, Köppen M, Franke K (eds) Hybrid Intelligent systems, Design and Application, IOS Press, The Netherlands pp 961-970
France DJ, Shivavi RG, Silverman S, Silverman M, Wilkes M (2000) Acoustical properties of speech as indicators of depression and suicidal risk. IEEE Trans Biomed Eng 7:829-837
Hyun KH, Kim EH, Kwak YK (2006) Emotion Recognition Using Frequency Ranges Sensitive to Emotion In: Proceeding of 3rd International Conference on Autonomous Robots and Agents, pp 119-124
McGilloway S, Cowie R, Douglas-Cowie E, Gielen S, Westerdijk M, Stroeve S (2000) Approaching automatic recognition of emotion from voice: A rough benchmark. In: Proceeding of the ISCA Workshop on Speech and Emotion, pp 207-212
Nwe TL, Foo SW, De Silva LC (2003) Speech emotion recognition using hidden markov model. Speech Communication, 41:603-623.
Pantic M, Rothkrantz L (2003) Toward an affect-sensitive multimodal humancomputer interaction. In: Trew RJ, Calder J (eds) Proceeding of the IEEE, IEEE, pp 1370-1390
Quatieri TF (2002) Discrete-Time Speech Signal Processing Principles and Practice, Prentice Hall, New Jersey.
Rabiner LR, Juang BH (1993) Fundamentals of Speech Recognition, Prentice Hall, Englewood Cliffs, New Jersey
Schiel F, Steininger S, Turk U (2002) The Smartkom multimodal copus at BAS. In: The 3rd International Conference on Language Resources and Evaluation, pp 35-41
Tolkmitt FJ, Scherer KR (1986) Effect of experimentally induced stress on vocal parameters. J Exp Psychol: Hum Percept Perform 12:302-313
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Hyun, K.H., Kim, E.H., Kwak, Y.K. (2007). Emotion Recognition Using Voice Based on Emotion-Sensitive Frequency Ranges. In: Mukhopadhyay, S.C., Gupta, G.S. (eds) Autonomous Robots and Agents. Studies in Computational Intelligence, vol 76. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73424-6_25
Download citation
DOI: https://doi.org/10.1007/978-3-540-73424-6_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73423-9
Online ISBN: 978-3-540-73424-6
eBook Packages: EngineeringEngineering (R0)