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.
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References
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: Berlin
Golz M, Sommer D, Chen M, Trutschel U, Mandic D (2007) Feature Fusion for the Detection of Microsleep Events. J VLSI Signal Proc Syst, 49:329–342
Harrison Y, Horne JA (1997) Sleep deprivation affects speech. Sleep 20:871–877
Whitmore J, Fisher S (1996) Speech during sustained operations. Speech Communication 20:55–70
Krajewski J, Kröger B (2007) Using prosodic and spectral characteristics for sleepiness detection. Interspeech Proc., Antwerp, Belgium, 2003, pp 1841–1844
Krajewski J, Wieland R, Batliner A (in press) An acoustic framework for detecting fatigue in human-computer interaction. ICCHP Proc., Linz, Austria, 2008
Nwe TL, Li H, Dong M (2006) Analysis and Detection of Speech under Sleep Deprivation. Interspeech Proc., Pittsburgh, USA, 2006, pp 17–21
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 2249–2252
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 240–245
Mierswa I, Morik K (2005) Automatic feature extraction for classifying audio data”. Machine Learning Journal 58:127–148
Schuller B, Reiter S, Rigoll G (2006) Evolutionary Feature Generation in Speech Emotion Recognition. ICME 2006, Toronto, Canada, 2006, pp 5–8
Åkerstedt T, Gillberg M (1990) Subjective and objective sleepiness in the active individual. International Journal of Neuroscience 52:29–37
Boersma P (2001) PRAAT, a system for doing phonetics by computer, Glot International 5:341–345
Witten IH, Frank E (2005) Data Mining: Practical machine learning tools and techniques. Morgan Kaufmann, San Francisco
Webber C L, Zbilut J P (1994) Dynamical assessment of physiological systems and states using recurrence plot strategies. Journal of Applied Physiology 76: 965–973
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Krajewski, J., Golz, M., Sommer, D., Wieland, R. (2009). Genetic Algorithm Based Feature Selection Applied on Predicting Microsleep from Speech. In: Vander Sloten, J., Verdonck, P., Nyssen, M., Haueisen, J. (eds) 4th European Conference of the International Federation for Medical and Biological Engineering. IFMBE Proceedings, vol 22. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89208-3_46
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DOI: https://doi.org/10.1007/978-3-540-89208-3_46
Publisher Name: Springer, Berlin, Heidelberg
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