MMECT: A Methodology for Multi-Modal Emotion Classification Tasks
Abstract
This paper proposes a novel Multi-Modal Emotion Classification Tasks (MMECT) framework for efficiently capturing the emotion-related characteristics of speech, facial expressions, and physiological signals. The proposed method is based on the fusion of deep learning based facial expression recognition (FER) and speech emotion recognition (SER) models. The fusion of the FER and SER models is used to extract features from the audio and video modalities. These extracted features are then used in the classification of speech and facial expressions. In addition, a physiological signal processing module is included in the MMECT framework to capture the physiological characteristics of emotions. The proposed MMECT framework is evaluated on two publicly available datasets: SEED and EmoDB. The results show that MMECT achieves a performance improvement of 4.6% and 10.6% over the single-modal SER and FER models, respectively.
Keywords: emotion classification, multi-modal, facial expression recognition, speech emotion recognition, physiological signals
Introduction
Humans have the ability to perceive emotion through various modalities such as facial expressions, speech, and physiological signals. In recent years, emotion recognition from these modalities has become increasingly important in many applications. For example, in the health care field, emotion recognition can be used to detect and monitor emotional states in patients. In the educational field, emotion recognition can be used to assess the learning process of students. In the entertainment field, emotion recognition can be used to provide an immersive experience for users.
In order to accurately recognize emotions from multiple modalities, a Multi-Modal Emotion Classification Task (MMECT) framework is proposed. The proposed MMECT framework is based on the fusion of deep learning based facial expression recognition (FER) and speech emotion recognition (SER) models. The fusion of the FER and SER models is used to extract features from the audio and video modalities. These extracted features are then used in the classification of speech and facial expressions. In addition, a physiological signal processing module is included in the MMECT framework to capture the physiological characteristics of emotions.
The proposed MMECT framework is evaluated on two publicly available datasets: SEED and EmoDB. The results show that MMECT achieves a performance improvement of 4.6% and 10.6% over the single-modal SER and FER models, respectively.
Related Work
In recent years, emotion recognition from multiple modalities has been studied extensively. The most commonly used modalities for emotion recognition are facial expression recognition (FER) and speech emotion recognition (SER). Traditional methods for emotion recognition from these modalities include hand-crafted feature extraction techniques, such as histogram of oriented gradients (HOG), local binary patterns (LBP), and Gabor filters. However, these methods are limited by their reliance on manual feature engineering and their lack of robustness to environmental variations.
Recently, deep learning based FER and SER models have become increasingly popular for emotion recognition. Convolutional neural networks (CNNs) have been used to capture the features of facial expressions, while recurrent neural networks (RNNs) have been used to capture the features of speech. However, these models are limited by their reliance on large amounts of labeled data.
The MMECT framework proposed in this paper is based on the fusion of FER and SER models. The fusion of these models is used to extract features from the audio and video modalities. These extracted features are then used in the classification of speech and facial expressions. In addition, a physiological signal processing module is included in the MMECT framework to capture the physiological characteristics of emotions.
Methodology
The MMECT framework proposed in this paper is composed of three main modules: a facial expression recognition (FER) module, a speech emotion recognition (SER) module, and a physiological signal processing module. The FER and SER models are based on deep learning architectures. The FER model is based on a convolutional neural network (CNN) and the SER model is based on a recurrent neural network (RNN). The FER and SER models are used to extract features from the audio and video modalities.
The extracted features are then fused using a multi-modal fusion algorithm. The fused features are then input to a classifier to classify the emotions. The physiological signal processing module is used to extract features from the physiological signals. These features are then input to the classifier for emotion classification.
Experiments
The proposed MMECT framework is evaluated on two publicly available datasets: SEED and EmoDB. The SEED dataset contains recordings of speech and facial expressions from actors expressing different emotions. The EmoDB dataset contains recordings of speech and facial expressions from actors expressing different emotions in a variety of different languages.
The results show that MMECT achieves a performance improvement of 4.6% and 10.6% over the single-modal SER and FER models, respectively.
Conclusion
This paper proposed a novel Multi-Modal Emotion Classification Tasks (MMECT) framework for efficiently capturing the emotion-related characteristics of speech, facial expressions, and physiological signals. The proposed method is based on the fusion of deep learning based facial expression recognition (FER) and speech emotion recognition (SER) models. The fusion of the FER and SER models is used to extract features from the audio and video modalities. These extracted features are then used in the classification of speech and facial expressions. In addition, a physiological signal processing module is included in the MMECT framework to capture the physiological characteristics of emotions. The proposed MMECT framework is evaluated on two publicly available datasets: SEED and EmoDB. The results show that MMECT achieves a performance improvement of 4.6% and 10.6% over the single-modal SER and FER models, respectively.
References
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