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Acoustic analysis of Emotional Modulation in Speech: from Emotionally Neutral to Emotionally Aroused state

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Author Affiliations

  • 1School of Studies in Forensic Science, Samrat Vikramaditya University, Ujjain, MP, India
  • 2School of Studies in Forensic Science, Samrat Vikramaditya University, Ujjain, MP, India
  • 3School of Studies in Forensic Science, Samrat Vikramaditya University, Ujjain, MP, India

Res. J. Forensic Sci., Volume 14, Issue (2), Pages 34-38, July,29 (2026)

Abstract

Speech emotion analysis is important in forensic phonetics and speech technology because emotions can change how speech sounds. This study looked at how feelings like anger, sadness, fear, and happiness affect the sound of speech by comparing them with calm speech. The research used a quantitative repeated-measures approach with 30 female students who speak Hindi and are between 18 and 24 years old. They were from Vidyottama Hostel at Vikram University in Ujjain. Speech samples were recorded using Audacity and analysed in Praat to look at pitch, loudness, speaking time, and pauses. The results showed that anger and happiness have increased pitch and loudness, whereas sadness produced softer and slower speech patterns. The study suggests that simple tools for analysing speech sounds can be useful in forensic and clinical work.

References

  1. Juslin, P. N., & Scherer, K. R. (2003). Vocal expression of affect. In R. J. Davidson, K. R. Scherer, & H. H. Goldsmith (Eds.), Handbook of affective sciences (pp. 433–456). Oxford University Press., undefined, undefined
  2. Hollien, H. (2002). Forensic voice identification. Academic Press., undefined, undefined
  3. Scherer, K. R. (2003). Vocal communication of emotion. Speech Communication, 40(1–2), 227–256. https://doi.org/10.1016/S0167-6393(02)00079-5, undefined, undefined
  4. Ladefoged, P., & Johnson, K. (2014). A course in phonetics (7th ed.). Cengage Learning., undefined, undefined
  5. Cowie, R., Douglas-Cowie, E., Tsapatsoulis, N., Votsis, G., Kollias, S., Fellenz, W., & Taylor, J. G. (2001). Emotion recognition in human-computer interaction. IEEE Signal Processing Magazine, 18(1), 32–80. https://doi.org/10.1109/79.911197, undefined, undefined
  6. Boersma, P., &Weenink, D. (2024). Praat: Doing phonetics by computer [Computer software]. University of Amsterdam. https://www.praat.org, undefined, undefined
  7. Mower, E., Mataric, M. J., & Narayanan, S. S. (2011). A framework for automatic human emotion classification using emotion profiles. IEEE Transactions on Audio, Speech, and Language Processing, 19(5), 1057–1070. https://doi.org/10.1109/TASL.2010.2057429, undefined, undefined
  8. Schuller, B., Batliner, A., Steidl, S., & Seppi, D. (2011). Recognising realistic emotions and affect in speech: State of the art and lessons learnt from the first challenge. Speech Communication, 53(9–10), 1062–1087. https://doi.org/10.1016/j.specom.2011.01.011, undefined, undefined
  9. Schuller, B., et al. (2025). Advances in multilingual speech emotion recognition. Proceedings of INTERSPEECH 2025., undefined, undefined
  10. Verma, S., & Gupta, N. (2026). Deep learning approaches for emotional speech classification. Speech Technology Review, 11(1), 55–72., undefined, undefined
  11. Kent, R. D., & Read, C. (2002). The acoustic analysis of speech (2nd ed.). Singular Publishing Group., undefined, undefined
  12. El Ayadi, M., Kamel, M. S., &Karray, F. (2011). Survey on speech emotion recognition. Pattern Recognition, 44(3), 572–587. https://doi.org/10.1016/j.patcog.2010.09.020, undefined, undefined
  13. Murray, I. R., & Arnott, J. L. (1993). Toward the simulation of emotion in synthetic speech. The Journal of the Acoustical Society of America, 93(2), 1097–1108. https://doi.org/10.1121/1.405558, undefined, undefined
  14. Banse, R., & Scherer, K. R. (1996). Acoustic profiles in vocal emotion expression. Journal of Personality and Social Psychology, 70(3), 614–636. https://doi.org/10.1037/0022-3514.70.3.614, undefined, undefined
  15. Audacity Team. (2024). Audacity [Computer software]. Muse Group. https://www.audacityteam.org, undefined, undefined
  16. Field, A. (2018). Discovering statistics using IBM SPSS statistics (5th ed.). Sage., undefined, undefined
  17. Titze, I. R. (2000). Principles of voice production. National Center for Voice and Speech., undefined, undefined
  18. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Sage Publications., undefined, undefined
  19. Harrington, J. (2010). Phonetic analysis of speech corpora. Wiley-Blackwell., undefined, undefined
  20. Lee, J., & Park, S. (2025). Vocal intensity and pitch dynamics in emotional speech production. Journal of Acoustic Communication, 19(3), 201–214., undefined, undefined
  21. Tiwari, A., & Rao, M. (2026). Acoustic variability in emotional Hindi speech. Proceedings of INTERSPEECH 2026., undefined, undefined
  22. Jurafsky, D., & Martin, J. H. (2021). Speech and language processing (3rd ed.). Pearson., undefined, undefined