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Exploring the Significance of Low Frequency Regions in Electroglottographic Signals for Emotion Recognition

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Advances in Signal Processing and Intelligent Recognition Systems (SIRS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 678))

Abstract

Electroglottographic (EGG) signals are acquired directly from the glottis. Hence EGG signals effectively represent the excitation source part of the human speech production system. Compared to speech signals, EGG signals are smooth and carry perceptually relevant emotional information. The work presented in this paper includes a sequence of experiments conducted on the emotion recognition system developed by the Gaussian Mixture Modeling (GMM) of perceptually motivated Mel Frequency Cepstral Coefficients (MFCC) features extracted from the EGG. The conclusions drawn from these experiments are two folds. (1) The 13 static MFCC features showed improved emotion recognition performance than 39 MFCC features with dynamic coefficients (by adding \(\varDelta \) and \(\varDelta \) \(\varDelta \)). (2) Low frequency regions in the EGG are emphasized by increasing the number of Mel filters for MFCC computation found to improve the performance of emotion recognition for EGG. These experimental results are verified on the EGG data available in the classic German emotional speech database (EmoDb) for four emotions such as (Anger, Happy, Boredom and Fear) apart from Neutral signals.

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Authors and Affiliations

  1. Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, Tamilnadu, India

    S. G. Ajay, D. Pravena, D. Govind & D. Pradeep

Authors
  1. S. G. Ajay
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  2. D. Pravena
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  3. D. Govind
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  4. D. Pradeep
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Corresponding author

Correspondence to S. G. Ajay .

Editor information

Editors and Affiliations

  1. School of CS/IT, Indian Institute of Information Technology and Management, Trivandrum, Kerala, India

    Sabu M. Thampi

  2. Department of Electrical and Computer Engineering, Ryerson University, Toronto, Ontario, Canada

    Sri Krishnan

  3. Department of Computer Science, University of Salamanca, Salamanca, Salamanca, Spain

    Juan Manuel Corchado Rodriguez

  4. Electronics and Communication Sciences Unit, Indian Statistical Institute, Kolkata, West Bengal, India

    Swagatam Das

  5. Department of Systems and Computer Networks, Wroclaw University of Science and Technology, Wroclaw, Poland

    Michal Wozniak

  6. Faculty of Engineering and Technology, Liverpool John Moores University, Liverpool, United Kingdom

    Dhiya Al-Jumeily

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Ajay, S.G., Pravena, D., Govind, D., Pradeep, D. (2018). Exploring the Significance of Low Frequency Regions in Electroglottographic Signals for Emotion Recognition. In: Thampi, S., Krishnan, S., Corchado Rodriguez, J., Das, S., Wozniak, M., Al-Jumeily, D. (eds) Advances in Signal Processing and Intelligent Recognition Systems. SIRS 2017. Advances in Intelligent Systems and Computing, vol 678. Springer, Cham. https://doi.org/10.1007/978-3-319-67934-1_28

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  • DOI: https://doi.org/10.1007/978-3-319-67934-1_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67933-4

  • Online ISBN: 978-3-319-67934-1

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