In terms of human-computer interaction, it is becoming more and more important to correctly understand the user’s emotional state in a conversation, so the task of multimodal emotion recognition (MER) ...

Inspired by the hierarchical cognitive architecture and the perception-action model (PAM), we propose that the internal status acts as a kind of common-coding representation which affects, mediates ...

Emotion recognition is an important task for computer to understand the human status in brain computer interface (BCI) systems. It is difficult to perceive the emotion of some disabled people through ...

An Enhanced Context-based Emotion Detection Model using RoBERTa Emotions are integral in conveying information in a particular context. For example, the most basic questions can have multiple answers, ...

An Emotion Type Informed Multi-Task Model for Emotion Cause Pair Extraction Emotion-Cause Pair Extraction (ECPE) aims to jointly extract emotion clauses and the corresponding cause clauses from a ...

We introduce Language2Gesture (L2G), a cross-modal generative model designed to predict head gesture animations directly from audio inputs. Unlike existing head gesture prediction models, L2G excels ...

Speech emotion recognition (SER) is detecting a person’s emotional state through their voice, a technique now widely applied across various fields. This paper aims to explore the significance of ...

To address the problem, we propose a weighted co-training framework for emotion recognition using a frequency-spatial diffusion transformer. We propose the EEG signal generation model by utilizing ...

Emotion Recognition in Conversation (ERC) has become a major area of interest in the field of Natural Language Processing (NLP). The aim of this task is to achieve accurate classification of the ...

Sentiment analysis (SA) and text emotion detection (TED) are two computer techniques used to analyze text. SA categorizes text into positive, negative, or neutral opinions, while TED can identify a ...

In medical image analysis, Federated Learning (FL) stands out as a key technology that enables privacy-preserved, decentralized data processing, crucial for handling sensitive medical data. Currently, ...

To handle the scarcity and heterogeneity of electroencephalography (EEG) data for Brain-Computer Interface (BCI) tasks, and to harness the power of large publicly available data sets, we propose Neuro ...