<p>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 Δ and Δ Δ ). (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.</p>
D. Pravena, Dr. Govind D., Pradeep, D., and Ajay, S. G., “Exploring the Significance of Low Frequency Regions in Electroglottographic Signals for Emotion Recognition”, International Symposium on Signal Processing and Intelligent Recognition Systems. 2017.