Multi-emotion and intensity-driven response generation for richer multimodal dialogue

Area of Science:

• Artificial Intelligence
• Human-Computer Interaction
• Natural Language Processing

Background:

• Human communication relies heavily on emotional expression, which is crucial for conveying meaning and intent.
• Current AI speech agents often lack the ability to understand or generate nuanced emotional responses, limiting natural interaction.
• There is a growing research focus on developing emotion-aware communication systems for more impactful AI interactions.

Purpose of the Study:

• To develop an AI framework capable of generating multi-emotional responses with precisely controlled intensity.
• To integrate multimodal information (text, audio, visual) for more accurate emotion and intensity recognition in dialogues.
• To enhance the naturalness and impact of AI-human interactions through emotion-aware response generation.

Main Methods:

• Designed a Multimodal Multi Emotion and Intensity-leaded Deliberation Decoder (MMEI-DD) framework.
• Utilized Transformer networks to encode multimodal knowledge and capture inter-modality representations.
• Implemented a two-step decoding mechanism to guide response generation with specified emotion and intensity.

Main Results:

• The proposed MMEI-DD framework successfully generates multi-emotion and intensity-guided responses.
• Expanding the multimodal feature set (MEIMD+) improved performance and reliability compared to existing methods.
• Integration of multimodal knowledge significantly enhances the AI's ability to produce emotionally nuanced outputs.

Conclusions:

• The MMEI-DD framework represents a significant advancement in generating emotionally intelligent AI responses.
• Multimodal integration is key to achieving precise control over emotion and intensity in AI-generated dialogue.
• The publicly available resources will facilitate further research in multimodal, emotion-aware AI communication.