Umit Yapanel
ABSTRACT
Emotion and stress/neutral detection based on an input audio stream has been a topic of interest in the literature with various applications. This paper reports on a preliminary study of stress/neutral detection based on naturalistic home environment recordings of children. One major motivation of the work is to add stress/neutral detection functionality into the LENA™ System [10]. The study started with an acted emotion database, and tested the acoustic feature of Mel-frequency cepstral coefficients and the Gaussian Mixture Model (GMM) for stress/neutral detection on this relatively simple database. The method was then applied to the adult speech segments automatically extracted from home recordings of children with the LENA System, achieving 72% accuracy for adult stress/neutral detection. The application of this new functionality to a large number of naturalistic home environment recordings of children reveals interesting and meaningful statistical differences among the families of typically developing children, language-delayed children, and children with Autism Spectrum Disorders (ASD). The result suggests the potential for stress/neutral detection, along with the LENA System, as an integrated solution for (i) quality assessment of the child language environment, (ii) monitoring language interventions for disordered children, or (iii) general psychological and behavioral research.
- F. Burkhardt, A. Paeschke, M. Rolfes, W. Sendlmeier, B. Weiss, "A Database of German Emotional Speech", Proceedings of the InterSpeech, 2005Google Scholar
- D. Ververidis, C. Kotropoulos, "A State of the Art Review on Emotional Speech Databases", Proc. Of 1st Rich Media Conference, Laussane, Switzerland, pp. 109--119, Oct, 2003Google Scholar
- M. Shami, W. Verhelst, "An Evaluation of the Robustness of Existing Supervised Machine Learning Approaches to the Classification of Emotions in Speech", Speech Communication, 2007 Google ScholarDigital Library
- A. Liberman, "Apparatus and Methods for Detecting Emotions in the Human Voice", US Patent No: 7,165,033 B1.Google Scholar
- J. Courtright, I. Courtright, "The perception of nonverbal vocal cues of emotional meaning by language-disordered and normal children", Journal of speech and hearing research, vol. 26 412--417, Sept. 1983Google ScholarCross Ref
- M. Forsell, "Acoustic Correlates of Perceived Emotions in Speech", Mater's Thesis in Speech Communication, School of Computer Science and Communication, Royal Institute of Technology, Stockholm, Sweden, 2007Google Scholar
- M. Grimm, K. Kroschel, S. Narayanan, "Support Vector Regression for automatic recognition of spontaneous emotions in speech", Proc. Of International Conference on Acoustics, Speech and Signal Processing, 2007Google Scholar
- G. Zhou, J. Hansen and J. Kaiser, "Methods for stress classification: nonlinear TEO and linear speech based features", Proc. Of International Conference on Acoustics, Speech And Signal Processing, 1999 Google ScholarDigital Library
- J. Hansen, S. Bou-Ghazale, "Getting Started with SUSAS: A Speech Under Simulated and Actual Stress Database", EUROSPEECH-97, vol. 4, pp. 1743--1746Google Scholar
- D. Xu, U. Yapanel, S. Gray, J. Gilkerson, J. Richards, J. Hansen "Signal Processing for Young Child Speech Language Development" 1st Workshop on Child, Computer and Interaction, Oct. 2008, Chania, Crete, Greece. Also available: http://www.lenafoundation.org/DownloadFile.aspx/pdf/SignalProcessing_ChildSpeechGoogle Scholar
- J. H. L. Hansen, "Analysis and Compensation of Speech under Stress and Noise for Environmental Robustness in Speech Recognition," Speech Communications, Special Issue on Speech Under Stress, vol. 20(2), pp. 151--170, November 1996 Google ScholarDigital Library
- B. D. Womack, J. H. L. Hansen, "Classification of Speech Under Stress using Target Driven Features," Speech Communications, Special Issue on Speech Under Stress, vol. 20(1--2), pp. 131--150, November 1996. Google ScholarDigital Library
- G. Zhou, J. H. L. Hansen, and J. F. Kaiser, "Nonlinear Feature Based Classification of Speech under Stress," IEEE Transactions on Speech&Audio Processing, vol. 9, no. 2, pp. 201--216, March 2001.Google ScholarCross Ref
- http://www.lenafoundation.org/DataServices/Database.aspx?sub=trueGoogle Scholar
Index Terms
- Preliminary study of stress/neutral detection on recordings of children in the natural home environment
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