Abstract
In this paper, a speech emotion recognition agent for mobile communication service is proposed. The proposed system can recognize five emotional states - neutral, happiness, sadness, anger, and annoyance from the speech captured by a cellular phone in real time and then it calculates the degree of affection such as love, truthfulness, weariness, trick, friendship of the person who you are interesting to know through the mobile phone. In general, a speech acquired by a cellular phone contains noise due to the mobile network and environmental noise. Thus it can causes serious performance degradation due to the distortion in emotional features of the query speech. In order to alleviate the effect of these noises, we adopt a MA (Moving Average) filter which has relatively simple structure and low computational complexity. Then a feature optimization method is implemented to further improve and stabilize the system performance. For a practical application, we create an agent that can measure the degree of affection from the person who you want to know on the mobile phone. Two pattern classification methods, k-NN and SVM with probability estimates, are compared for estimating the degree of affection. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 72.5% over five emotional states and it shows the feasibility of the agent for mobile communication services.
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
Dellaert, F., Polzin, T., Waibel, A.: Recognizing emotion in Speech. In: Proc. International Conf. on Spoken Language Processing, pp. 1970–1973 (1996)
Scherer, K.R.: Adding the affective dimension-A new look in speech analysis and synthesis. In: Proc. International Conf. on Spoken Language Processing, pp. 1808–1811 (1996)
Zhou, G., Hansen, J.H.L., Kaiser, J.F.: Nonlinear Feature Based Classification of Speech Under Stress. IEEE Transactions on speech and audio processing 9(3) (2001)
Yacoub, S., Simske, S., Lin, X., Burns, J.: Recognition of emotions in interactive voice response system. In: Eurospeech 2003 Proc. (2003)
Kostov, V., Fukuda, S.: Emotion in user interface. Voice Interaction system. no. 2. In: IEEE Intl. Conf. on systems, Man, Cybernetics Representation, pp. 798–803 (2000)
Oriyama, T.M., Oazwa.: Emotion recognition and synthesis system on speech. In: IEEE Intl. Conference on Multimedia Computing and Systems, pp. 840–844. IEEE Computer Society Press, Los Alamitos (1999)
Lee, C.M., Narayanan, S., Pieraccini, R.: Classifying emotions in human-machine spoken dialogs. In: ICME 2002 (2002)
Wu, T.-F., Lin, C.-J., Weng, R.C.: Probability Estimates for Multi-class Classification by Pairwise Coupling. Journal of Machine Learning Research (2004)
Gu, L., Zahorian, S.A.: A new robust algorithm for isolated word end-point detection. In: ICASSP 2002, Orlando, USA (2002)
Noll, M.: Pitch determination of human speech by the harmonic product spectrum, the harmonic sum spectrum, and a maximum likelihood estimate. In: Proceedings of the Symposium on Computer Processing Communications, pp. 779–797 (1969)
Ross, M.J., Shaer, H.L., Cohen, A., Freudberg, R., Manley, H.J.: Average magnitude difference function pitch extractor. ASSP-22, 353–362 (1974)
Sun, X.: A pitch determination algorithm based on subharmonic-to harmonic ratio. In: ICSLP, pp. 676–679 (2000)
Liu, M., Wan, C.: A study on content-based classification retrieval of audio database. In: Proc. of the International Database Engineering & Applications Symposium, pp. 339–345 (2001)
Bong-Seok, K.: A text-independent emotion recognition algorithm using speech signal. MS Thesis, Yonsei University (2000)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yoon, WJ., Cho, YH., Park, KS. (2007). A Study of Speech Emotion Recognition and Its Application to Mobile Services. In: Indulska, J., Ma, J., Yang, L.T., Ungerer, T., Cao, J. (eds) Ubiquitous Intelligence and Computing. UIC 2007. Lecture Notes in Computer Science, vol 4611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73549-6_74
Download citation
DOI: https://doi.org/10.1007/978-3-540-73549-6_74
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73548-9
Online ISBN: 978-3-540-73549-6
eBook Packages: Computer ScienceComputer Science (R0)