Genetic Algorithm Based Feature Selection Applied on Predicting Microsleep from Speech

  • J Krajewski
  • M. Golz
  • D. Sommer
  • R. Wieland
Part of the IFMBE Proceedings book series (IFMBE, volume 22)

Abstract

Within this study we apply a speech emotion recognition engine on the detection of microsleep endangered sleepiness states. Current approaches in speech emotion recognition use low-level descriptors and functionals to compute brute-force feature sets. This paper describes an usually large feature set (45k) utilizing a broad pool of diverse elementary statistics and spectral descriptors. Several (un-)supervised subset selection methods including genetic algorithm based methods were employed on the feature space in an attempt to prune redundant dimensions. The resulting dimensionality reduced feature space was applied to speech samples gained from a car simulator based sleep deprivation study (N=12; 01.00–08.00 a.m.). Among the tested dimensionality reduction methods a simple correlation filter approach (130 features remaining) reached the best recognition rate (85.1%, SVM) in predicting microsleep endangered sleepiness stages.

Keywords

Acoustic Features Sleepiness Detection Feature Selection Genetic Algorithm 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sommer D, Chen M, Golz M, Trutschel U, Mandic D (2005) Fusion of State Space and Frequency Domain Features for Improved Microsleep Detection. In W Dutch et al. (Eds.) Int Conf Artifical Neural Networks (ICANN 200), pp 753–759. Springer: BerlinGoogle Scholar
  2. 2.
    Golz M, Sommer D, Chen M, Trutschel U, Mandic D (2007) Feature Fusion for the Detection of Microsleep Events. J VLSI Signal Proc Syst, 49:329–342CrossRefGoogle Scholar
  3. 3.
    Harrison Y, Horne JA (1997) Sleep deprivation affects speech. Sleep 20:871–877Google Scholar
  4. 4.
    Whitmore J, Fisher S (1996) Speech during sustained operations. Speech Communication 20:55–70CrossRefGoogle Scholar
  5. 5.
    Krajewski J, Kröger B (2007) Using prosodic and spectral characteristics for sleepiness detection. Interspeech Proc., Antwerp, Belgium, 2003, pp 1841–1844Google Scholar
  6. 6.
    Krajewski J, Wieland R, Batliner A (in press) An acoustic framework for detecting fatigue in human-computer interaction. ICCHP Proc., Linz, Austria, 2008Google Scholar
  7. 7.
    Nwe TL, Li H, Dong M (2006) Analysis and Detection of Speech under Sleep Deprivation. Interspeech Proc., Pittsburgh, USA, 2006, pp 17–21Google Scholar
  8. 8.
    Vlasenk B, Schuller B, Wendemuth A, Rigoll G (2007) Combining Frame and Turn-Level Information for Robust Recognition of Emotions within Speech. Interspeech Proc., Antwerp, Belgium, pp 2249–2252Google Scholar
  9. 9.
    Batliner A, Steidl S, Schuller B, Seppi D, Laskowski K, Vogt T, Devillers L, Vidrascu L, Amir N, Kessous L, Aharonson V (2006) Combining Efforts for Improving Automatic Classification of Emotional User States. In Erjavec T & Gros JZ (Eds.): Language Technologies, IS-LTC 2006, Ljubljana, Slovenia, pp 240–245Google Scholar
  10. 10.
    Mierswa I, Morik K (2005) Automatic feature extraction for classifying audio data”. Machine Learning Journal 58:127–148MATHCrossRefGoogle Scholar
  11. 11.
    Schuller B, Reiter S, Rigoll G (2006) Evolutionary Feature Generation in Speech Emotion Recognition. ICME 2006, Toronto, Canada, 2006, pp 5–8Google Scholar
  12. 12.
    Åkerstedt T, Gillberg M (1990) Subjective and objective sleepiness in the active individual. International Journal of Neuroscience 52:29–37CrossRefGoogle Scholar
  13. 13.
    Boersma P (2001) PRAAT, a system for doing phonetics by computer, Glot International 5:341–345Google Scholar
  14. 14.
    Witten IH, Frank E (2005) Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San FranciscoMATHGoogle Scholar
  15. 15.
    Webber C L, Zbilut J P (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology 76: 965–973Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • J Krajewski
    • 1
  • M. Golz
    • 2
  • D. Sommer
    • 2
  • R. Wieland
    • 1
  1. 1.Work and Organizational PsychologyUniv. of WuppertalWuppertalGermany
  2. 2.Neuroinformatics and Signal ProcessingUniv. of Applied Sciences SchmalkaldenSchmalkaldenGermany

Personalised recommendations