Audiovisual emotion recognition in wild

Egils Avots · Tomasz Sapiński · Maie Bachmann · Dorota Kamińska

161 citations0/0 files storedMachine Vision and Applications

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Emotion and Mood Recognition Speech and Audio Processing Face and Expression Recognition

Abstract

People express emotions through different modalities. Utilization of both verbal and nonverbal communication channels allows to create a system in which the emotional state is expressed more clearly and therefore easier to understand. Expanding the focus to several expression forms can facilitate research on emotion recognition as well as human–machine interaction. This article presents analysis of audiovisual information to recognize human emotions. A cross-corpus evaluation is done using three different databases as the training set (SAVEE, eNTERFACE’05 and RML) and AFEW (database simulating real-world conditions) as a testing set. Emotional speech is represented by commonly known audio and spectral features as well as MFCC coefficients. The SVM algorithm has been used for classification. In case of facial expression, faces in key frames are found using Viola–Jones face recognition algorithm and facial image emotion classification done by CNN (AlexNet). Multimodal emotion recognition is based on decision-level fusion. The performance of emotion recognition algorithm is compared with the validation of human decision makers.

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References

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2020Automated accurate speech emotion recognition system using twine shuffle pattern and iterative neighborhood component analysis techniques 20193D CNN-Based Speech Emotion Recognition Using K-Means Clustering and Spectrograms 2021Attention guided 3D CNN-LSTM model for accurate speech based emotion recognition 2021A multimodal hierarchical approach to speech emotion recognition from audio and text 2025A Comprehensive Review of Multimodal Emotion Recognition: Techniques, Challenges, and Future Directions

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