Multi-modal conversational AI for realistic human-like communication
Word Count : 3000
Introduction: Overview of multi-modal conversational AI for human-like interaction.
Background & Motivation: Importance of combining text, voice, and vision for natural communication.
System Architecture: Framework integrating speech, vision, and language processing modules.
Representation Learning: Unified multi-modal embedding and feature fusion techniques.
Emotion & Context Understanding: Detecting user emotion, sentiment, and conversational context.
Real-Time Response Generation: Pipeline for fast and natural conversational responses.
Training Approaches: Self-supervised and reinforcement learning methods for model improvement.
Evaluation Metrics: Performance measures for realism, naturalness, and accuracy.
Conclusion: Summary, challenges, and future scope of multi-modal human-like conversational AI.