A Novel Multimodal Deep Learning Framework for Conversational <scp>AI</scp> : Integrating Vision, Text, and Speech With Knowledge‐Augmented Attention Mechanisms

ABSTRACT Existing conversational AI models lack effective multimodal fusion, leading to poor contextual understanding and factual inconsistencies. This research proposes a novel multimodal deep learning framework that integrates vision (CNNs and Vision Transformers), text (Transformers like BERT and GPT‐4), and speech (RNNs and Self‐Supervised Learning) using an attention‐based fusion mechanism. The model enhances sarcasm detection and sentiment analysis through contrastive learning, while graph neural networks (GNNs) ensure knowledge‐augmented reasoning, reducing hallucinations in AI‐generated responses. Reinforcement learning optimizes dialogue generation for more natural and contextually accurate interactions. Experimental evaluations on MELD, CMU‐MOSEI, and AVSD datasets demonstrate a 9%–12% improvement in classification accuracy, 15% higher sarcasm detection efficiency, and a 23% reduction in factual inconsistencies compared to state‐of‐the‐art models like GPT‐4, CLIP, and Flamingo. Extensive benchmarking using precision‐recall analysis and response fluency metrics validates the robustness of the proposed framework in multimodal sentiment classification and contextual alignment. This research lays the foundation for next‐generation multimodal conversational AI, with potential applications in real‐world domains such as virtual assistants, healthcare diagnostics, and financial intelligence systems.