An epitomization of stress recognition from speech signal

  • Authors

    • Veena Narayanan Amrita Vishwa Vidyapeetham
    • S Lalitha Amrita Vishwa Vidyapeetham
    • Deepa Gupta Amrita Vishwa Vidyapeetham
    2018-08-02
    https://doi.org/10.14419/ijet.v7i2.27.10123
  • Stress Recognition, Feature Extraction, Statistical Measures, MFCC, LPCC.

  • The Detection of stress from speech signal is gaining large attention recently. The emergence of new methods and techniques for feature extraction and classification paved the way to different solutions to detect different stress conditions using human speech and led to an in-crease in the accuracy of stress recognition. A large number of parameters are proposed for the characterization of stress in speech. Similarly numerous classifiers and machine learning algorithms are investigated for stress classification and regression. In this treatise, a recital on the commonly used databases, stress conditions, different feature extraction methods and classifiers along with some of the statistical measures as well as compensation techniques for stress detection are presented in this article. After thorough illustration of existing methodology for the task, future prospects for the work are elaborated.

     

     

     

     

  • References

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    Narayanan, V., Lalitha, S., & Gupta, D. (2018). An epitomization of stress recognition from speech signal. International Journal of Engineering & Technology, 7(2.27), 61-68. https://doi.org/10.14419/ijet.v7i2.27.10123