Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers

• URL: http://arxiv.org/abs/2402.07327v1
• Date: Sun, 11 Feb 2024 23:27:24 GMT
• Title: Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers
• Authors: Minoo Shayaninasab, Bagher Babaali
• Abstract summary: Three input modalities, namely text, audio (speech), and video, are employed to generate multimodal feature vectors. For generating features for each of these modalities, pre-trained Transformer models with fine-tuning are utilized. The best model, which combines feature-level fusion by concatenating feature vectors and classification using a Support Vector Machine, achieves an accuracy of 75.42%.
• Score: 1.0152838128195467
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Due to the complex nature of human emotions and the diversity of emotion representation methods in humans, emotion recognition is a challenging field. In this research, three input modalities, namely text, audio (speech), and video, are employed to generate multimodal feature vectors. For generating features for each of these modalities, pre-trained Transformer models with fine-tuning are utilized. In each modality, a Transformer model is used with transfer learning to extract feature and emotional structure. These features are then fused together, and emotion recognition is performed using a classifier. To select an appropriate fusion method and classifier, various feature-level and decision-level fusion techniques have been experimented with, and ultimately, the best model, which combines feature-level fusion by concatenating feature vectors and classification using a Support Vector Machine on the IEMOCAP multimodal dataset, achieves an accuracy of 75.42%. Keywords: Multimodal Emotion Recognition, IEMOCAP, Self-Supervised Learning, Transfer Learning, Transformer.

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