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CMSBERT-CLR: Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations
In this paper, we present a Context-driven Modality Shifting BERT with Contrastive Learning for linguistic, visual, acoustic Representations (CMSBERT-CLR), which incorporates the whole context's non-verbal and verbal information and aligns modalities more effectively through contrastive learning.
Improving Speech Emotion Recognition Through Focus and Calibration Attention Mechanisms
To improve the attention area, we propose to use a Focus-Attention (FA) mechanism and a novel Calibration-Attention (CA) mechanism in combination with the multi-head self-attention.
Representation Learning with Graph Neural Networks for Speech Emotion Recognition
In particular, we propose a cosine similarity-based graph as an ideal graph structure for representation learning in SER.
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