Skip to main content
SpringerLink
Log in

An ICA-based method for stress classification from voice samples

  • Developing nature-inspired intelligence by neural systems
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Emotion detection is a hot topic nowadays for its potential application to intelligent systems in different fields such as neuromarketing, dialogue systems, friendly robotics, vending platforms and amiable banking. Nevertheless, the lack of a benchmarking standard makes it difficult to compare results produced by different methodologies, which could help the research community improve existing approaches and design new ones. Besides, there is the added problem of accurate dataset production. Most of the emotional speech databases and associated documentation are either privative or not publicly available. Therefore, in this work, two stress-elicited databases containing speech from male and female speakers were recruited, and four classification methods are compared in order to detect and classify speech under stress. Results from each method are presented to show their quality performance, besides the final scores attained, in what is a novel approach to the field of study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  1. Ekman P (1999) Basic emotions. In: Dalgleish T, Power MJ (eds) Handbook of cognition and emotion. Wiley, Chichester, pp 45–60

    Google Scholar 

  2. Plutchik R (1980) A general psychoevolutionary theory of emotion, in theories of emotion. Academic Press, New York, pp 3–33

    Google Scholar 

  3. Plutchik R (1994) The psychology and biology of emotion. HarperCollins College Publishers, New York

    Google Scholar 

  4. Ortony A, Turner TJ (1990) What’s basic about basic emotions? Psychol Rev 97(3):315–331

    Article  Google Scholar 

  5. Darwin C (1998) The expression of emotions in man and animals, 3rd edn. Oxford University Press, Oxford

    Google Scholar 

  6. National Research Council (US) Committee on Pain and Distress in Laboratory Animals (1992) Recognition and alleviation of pain and distress in laboratory animals. National Academies Press, Washington. https://doi.org/10.17226/1542

    Book  Google Scholar 

  7. Russ TC, Stamatakis E, Hamer M, Starr JM, Kivimaki M, Batty GD (2012) Association between psychological distress and mortality: individual participant pooled analysis of 10 prospective cohort studies. BMJ 345:e4933. https://doi.org/10.1136/bmj.e4933

    Article  Google Scholar 

  8. Gonzalez RC, Woods RE (1992) Digital imaging processing. Addison-Wesley, Upper Saddle River, pp 52–54

    Google Scholar 

  9. Ververidis D, Kotropoulos C (2003) A review of emotional speech databases. In: Proceedings of Panhellenic conference on informatics (PCI), pp 560–574

  10. Koolagudi SG, Rao KS (2012) Emotion recognition from speech: a review. Int J Speech Technol 15(2):99–117

    Article  Google Scholar 

  11. Ayadi MS, Kamel MS, Karray F (2011) Survey on speech emotion recognition: features, classification schemes, and databases. Pattern Recognit 44(3):572–587

    Article  Google Scholar 

  12. Zuo X, Lin L, Fung P (2012) A multilingual database of natural stress emotion. In: Proceedings of the 8th international conference on language resources and evaluation (LREC’12), pp 1174–1178

  13. Fernandez R, Picard RW (2003) Modeling drivers’ speech under stress. Speech Commun 40(1):145–159

    Article  Google Scholar 

  14. Hansen JH, Bou-Ghazale SE, Sarikaya R, Pellom B (1997) Getting started with SUSAS: a speech under simulated and actual stress database. Eurospeech, pp 1743–1746

  15. Sigmund M (2006) Introducing the database ExamStress for speech under stress. In: Proceedings of the 7th Nordic signal processing symposium NORSIG 2006, pp 290–293

  16. Scherer S, Hofmann H, Lampmann M, Pfeil M, Rhinow S, Schwenker F, Palm G (2008) Emotion recognition from speech: stress experiment. In: Proceedings of the 6th international language resources and evaluation (LREC)

  17. Arciuli J, Villar G, Mallard D (2009) Lies, lies and more lies. In: Proceedings of the 31st annual conference of the cognitive science society (CogSci 2009), pp 2329–2334

  18. Rodellar-Biarge V, Palacios-Alonso D, Nieto-Lluis V, Gomez-Vilda P (2015) Analysis of emotional stress in voice for deception detection. In: Proceedings of the international work conference on bioinspired intelligence (IWOBI), pp 127–132

  19. BioMet®Phon: www.glottex.com. Accessed 21 July 2019

  20. Hyvärinen A, Oja H (2000) Independent component analysis: algorithms and applications. Neural Netw 13(4):411–430

    Article  Google Scholar 

  21. Jutten C, Herault J (1991) Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Proc 24(1):1–10

    Article  Google Scholar 

  22. Martínez-Murcia FJ, Górriz JM, Ramírez J, Puntonet CG, Illán IA, ADNI (2013) Functional activity maps based on significance measures and independent component analysis. Comput Methods Programs Biomed 111(1):255–268

    Article  Google Scholar 

  23. Bartlett MS, Movellan JR, Sejnowski TJ (2002) Face recognition by independent component analysis. IEEE Trans Neural Netw 13(6):1450–1464

    Article  Google Scholar 

  24. Hyvärinen A (1999) Fast and robust fixed-point algorithms for independent component analysis. IEEE Trans Neural Netw 10(3):626–634

    Article  Google Scholar 

  25. Hyvärinen A, Oja E (1997) A fast fixed-point algorithm for independent component analysis. Neural Comput 9(7):1483–1492

    Article  Google Scholar 

  26. Jolliffe IT (2002) Principal component analysis. Springer, New York

    MATH  Google Scholar 

  27. López M, Ramírez J, Górriz JM, Álvarez I, Salas-Gonzalez D, Segovia F, Chaves R, Padilla P, Gómez-Río M, ADNI (2011) Principal component analysis-based techniques and supervised classification schemes for the early detection of Alzheimer’s disease. Neurocomputing 74(8):1260–1271

    Article  Google Scholar 

  28. Lee CM, Narayanan S, Pieraccini R (2001) Recognition of negative emotions from the speech signal. In: Proceedings of IEEE workshop on automatic speech recognition and understanding ASRU ‘01, pp 240–243

  29. Vapnik VN, Vapnik V (1998) Statistical learning theory. Wiley, New York

    MATH  Google Scholar 

  30. Burges CJ (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Disc 2(2):121–167

    Article  Google Scholar 

  31. Illán IA, Górriz JM, Ramírez J, Salas-Gonzalez D, López MM, Segovia F, Chaves R, Gómez-Rio M, Puntonet CG, ADNI (2011) 18F-FDG PET imaging analysis for computer aided Alzheimer’s diagnosis. Inf Sci 181(4):903–916

    Article  Google Scholar 

  32. Rodellar-Biarge V, Palacios-Alonso D, Nieto-Lluis V, Gómez-Vilda P (2015) Towards the search of detection in speech-relevant features for stress. Expert Syst 32(6):710–718

    Article  Google Scholar 

  33. Tan S, Zhang J (2008) An empirical study of sentiment analysis for Chinese documents. Expert Syst Appl 34(4):2622–2629

    Article  Google Scholar 

  34. Gómez-Vilda P, Nieto-Lluis Rodellar-Biarge V, Álvarez-Marquina A, Mazaira-Fernández LM, Martínez-Olalla R, Muñoz-Mulas C, Fernández-Fernández M, Ramírez-Calvo C (2013) Estimating tremor in vocal fold biomechanics for neurological disease characterization. In: Proceedings of the 18th international conference on digital signal processing (DSP 2013), pp 1–6

  35. Rodellar-Biarge V, Palacios-Alonso D, Bartolomé-Morala E, Gómez-Vilda P (2013) Vocal fold stiffness estimates for emotion description in speech. In: Proceedings of biosignals, pp 112–119

Download references

Acknowledgements

This work has been funded by Grants TEC2016-77791-C4-4-R from the Ministry of Economic Affairs and Competitiveness of Spain and Teca-Park/MonParLoc FGCSIC CENIE-0348_CIE_6_E (InterReg Programme) V-A Spain – Portugal (POCTEP) (Grant No. CENIE_TECA-PARK_55_02).

Author information

Authors and Affiliations

  1. Center for Biomedical Technology, Universidad Politécnica de Madrid, Campus de Montegancedo, 28223, Pozuelo de Alarcón, Madrid, Spain

    Daniel Palacios, Victoria Rodellar, Carlos Lázaro, Andrés Gómez & Pedro Gómez

  2. Escuela Técnica Superior de Ingeniería Informática, Universidad Rey Juan Carlos, Calle Tulipán, s/n, 28933, Móstoles, Madrid, Spain

    Daniel Palacios & Carlos Lázaro

Authors
  1. Daniel Palacios
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Victoria Rodellar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Carlos Lázaro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Andrés Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pedro Gómez
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pedro Gómez.

Ethics declarations

Conflict of interest

The authors declare not having any conflict of interest and that their research has been conducted in compliment with all ethical principles.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Palacios, D., Rodellar, V., Lázaro, C. et al. An ICA-based method for stress classification from voice samples. Neural Comput & Applic 32, 17887–17897 (2020). https://doi.org/10.1007/s00521-019-04549-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-019-04549-3

Keywords

Search

Navigation