Efficient speech emotion recognition using modified feature extraction

Shadi Langari · Hossein Marvi · Morteza Zahedi

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Emotion and Mood Recognition Speech Recognition and Synthesis Speech and Audio Processing

Abstract

One of the most important issues in human-computer interaction is to create a system that can hear and respond correctly like a human. This has led to the design of the Automatic Speech Emotion Recognition system (SER) that is able to identify different emotional classes by extracting and selecting effective features from speech signals. For this reason, in this study, we propose a novel feature extraction method based on adaptive time-frequency coefficients to improve the SER. The simulations are performed using the Berlin Emotional Speech Database (EMO-DB), the Surrey Audio-Visual Expressed Emotion Database (SAVEE), and the Persian Drama Radio Emotional Corpus (PDREC). The main contribution of our work is to extract novel features, called Adaptive Time-Frequency features, based on the Fractional Fourier Transform and to combine them with Cepstral features. Experimental results show that the proposed method effectively identifies different emotional classes in EMO-DB (97.57% accuracy), SAVEE (80% accuracy), and PDREC (91.46% accuracy) data sets.

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References

2005A database of German emotional speech 2010Survey on speech emotion recognition: Features, classification schemes, and databases 2010Affect Detection: An Interdisciplinary Review of Models, Methods, and Their Applications 2005Toward detecting emotions in spoken dialogs 2009The INTERSPEECH 2009 emotion challenge 2003Speech emotion recognition using hidden Markov models 2019Speech emotion recognition: Emotional models, databases, features, preprocessing methods, supporting modalities, and classifiers 2014Learning Salient Features for Speech Emotion Recognition Using Convolutional Neural Networks

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