The Acoustic Cues of Fear : Investigation of Acoustic Parameters of Speech Containing Fear

• Autorzy: Özseven T.

Identyfikatory

DOI

10.24425/122372

• Języki publikacji: EN

Abstrakty

EN

Speech emotion recognition is an important part of human-machine interaction studies. The acoustic analysis method is used for emotion recognition through speech. An emotion does not cause changes on all acoustic parameters. Rather, the acoustic parameters affected by emotion vary depending on the emotion type. In this context, the emotion-based variability of acoustic parameters is still a current field of study. The purpose of this study is to investigate the acoustic parameters that fear affects and the extent of their influence. For this purpose, various acoustic parameters were obtained from speech records containing fear and neutral emotions. The change according to the emotional states of these parameters was analyzed using statistical methods, and the parameters and the degree of influence that the fear emotion affected were determined. According to the results obtained, the majority of acoustic parameters that fear affects vary according to the used data. However, it has been demonstrated that formant frequencies, mel-frequency cepstral coefficients, and jitter parameters can define the fear emotion independent of the data used.

Słowa kluczowe

EN

emotion recognition; acoustic analysis; fear; speech processing

• Wydawca: Instytut Podstawowych Problemów Techniki PAN ; Komitet Akustyki PAN ; Polskie Towarzystwo Akustyczne
• Czasopismo: Archives of Acoustics
• Rocznik: 2018
• Tom: Vol. 43, No. 2
• Strony: 245--251
• Opis fizyczny: Bibliogr. 27 poz., tab.

Twórcy

autor

Özseven T.

• Department of Computer Engineering, Gaziosmanpaşa University, Tokat, Turkey

Bibliografia

• 1. Althouse L. A., Ware W. B., Ferron J. M. (1998), Detecting departures from normality: a monte carlo simulation of a new omnibus test based on moments, presented at the Annual Meeting of the American Educational Research Association, San Diego, California, https://eric.ed.gov/?id=ED422385.
• 2. Arnold M. B. (1960), Emotion and personality, Columbia University Press, New York.
• 3. Boersma P., Weenink D. (2002), Praat, a system for doing phonetics by computer, Glot International, 5, 9-10, 341-345.
• 4. Burkhardt F., Paeschke A., Rolfes M., Sendlmeier W. F., Weiss B. (2005), A database of German emotional speech, Interspeech, 5, 1517-1520.
• 5. Costantini G., Iaderola I., Paoloni A., Todisco M. (2014), EMOVO Corpus: an Italian Emotional Speech Database, Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14), pp. 3501-3504.
• 6. Deshmukh O., Espy-Wilson C. Y., Salomon A., Singh J. (2005), Use of temporal information: Detection of periodicity, aperiodicity, and pitch in speech, IEEE Transactions on Speech Audio Process, 13, 5, 776-786.
• 7. Diamond G. M., Rochman D., Amir O. (2010), Arousing primary vulnerable emotions in the context of unresolved anger:“Speaking about” versus “speaking to”, Journal of Counseling Psychology, 57, 4, 402-410.
• 8. Drioli C., Tisato G., Cosi P., Tesser F. (2003), Emotions and voice quality: experiments with sinusoidal modeling, [in:] Voice Quality: Functions, Analysis and Synthesis (VOQUAL’03), ISCA Tutorial and Research Workshop, Christophe d’Alessandro, Klaus R. Scherer [Eds.], Geneva, Switzerland, August 27-29, 2003, ISCA Archive, http://www.iscaspeech.org/archive_open/voqual03, pp. 127-132.
• 9. Farrus M., Hernando J. (2009), Using jitter and shimmer in speaker verification, IET Signal Processing, 3, 4, 247-257.
• 10. Fuller B. F., Horii Y., Conner D. A. (1992), Validity and reliability of nonverbal voice measures as indicators of stressor-provoked anxiety, Research in Nursing and Health, 15, 5, 379-389.
• 11. Goberman A. M., Hughes S., Haydock T. (2011), Acoustic characteristics of public speaking: anxiety and practice effects, Speech Communication, 53, 6, 867-876.
• 12. Hagenaars M. A., van Minnen A. (2005), The effect of fear on paralinguistic aspects of speech in patients with panic disorder with agoraphobia, Journal of Anxiety Disorders, 19, 5, 521-537.
• 13. Kılıç M. A., Okur E. K. M. (2001), Comparison of fundamental frequency and perturbation data analyzed by CSL and Dr. Speech systems, The Turkish Journal of Ear Nose and Throat, 8, 2, 152-157.
• 14. Laukka P. et al. (2008), In a nervous voice: Acoustic analysis and perception of anxiety in social phobics’ speech, Journal of Nonverbal Behavior, 32, 195-214.
• 15. Mendes M., Pala A. (2003), Type I error rate and power of three normality tests, Information Technology Journal, 2, 2, 135-139.
• 16. Murray I. R., Arnott J. L. (1993), Toward the simulation of emotion in synthetic speech: A review of the literature on human vocal emotion, Journal of Acoustical Society of America, 93, 2, 1097-1108.
• 17. Protopapas A., Lieberman P. (1997), Fundamental frequency of phonation and perceived emotional stress, Journal of Acoustical Society of America, 101, 4, 2267-2277.
• 18. Razali N. M., Wah Y. B. (2011), Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests, Journal of Statistical Modeling and Analytics, 2, 1, 21-33.
• 19. Ruiz R., Absil E., Harmegnies B., Legros C., Poch D. (1996), Time-and spectrum-related variabilities in stressed speech under laboratory and real conditions, Speech Communication, 20, 1-2, 111-129.
• 20. Sarica S. (2012), Acoustic parameters that used in voice analysis, PhD Thesis, Kahramanmaras Sutcu Imam University.
• 21. Scherer K. R. (1982), Emotion as a process: Function, origin and regulation, Social Science Information, 21, 4, 555-570.
• 22. Scherer K. R. (1984), On the nature and function of emotion: a component process approach, [in:] Klaus R. Scherer, Paul Ekman [Eds.], Approaches to emotion, pp. 293-317, Hillsdale, NJ: Erlbaum.
• 23. Sethu V. (2009), Automatic emotion recognition: an investigation of acoustic and prosodic parameters, PhD Thesis, The University of New South Wales.
• 24. Shapiro S. S., Wilk M. B. (1965), An analysis of variance test for normality (complete samples), Biometrika, 52, 3-4, 591-611.
• 25. Tompkins S. (1962), Affect Imagery Consciousness. Vol. I: The Positive Affects, Springer Publishing Company.
• 26. Ververidis D., Kotropoulos C. (2006), Emotional speech recognition: Resources, features, and methods, Speech Communication, 48, 9, 1162-1181, doi: 10.1016/j.specom.2006.04.003.
• 27. Weeks J. W. et al. (2012), “The sound of fear”: Assessing vocal fundamental frequency as a physiological indicator of social anxiety disorder, Journal of Anxiety Disorders, 26, 8, 811-822.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).