Abstract
As technological advancements progress, dependence on machines is inevitable. Therefore, to facilitate effective interaction between humans and machines, it has become crucial to develop proficient techniques for Speech Emotion Recognition (SER). This paper uses phase-based features, namely Modified Group Delay Cepstral Coefficients for SER. To the best of our knowledge, this paper is the first attempt to use the MGDCC feature on emotions. Experiments were performed using the EmoDB database on emotions, anger, happy, neutral, and sad. The proposed feature outperformed the baseline Mel Frequency Cepstral Coefficients (MFCC) and Linear Frequency Cepstral Coefficients (LFCC) by 7.7 % and 5.14 %, respectively. The noise robustness characteristics of MGDCC were tested on stationary and non-stationary noise and the results were promising. The latency period was also analysed and MGDCC proved to be the most practically suitable feature.
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Uthiraa, S., Pusuluri, A., Patil, H.A. (2023). Modified Group Delay Features for Emotion Recognition. In: Maji, P., Huang, T., Pal, N.R., Chaudhury, S., De, R.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2023. Lecture Notes in Computer Science, vol 14301. Springer, Cham. https://doi.org/10.1007/978-3-031-45170-6_33
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