Geospatial AIoT for Personalized Slow Tourism Experiences Meeting Urban Senior Care Needs and Promoting Mental Well-Being

Inclusive communication remains a critical challenge for individuals with hearing impairments, speech disorders, or multilingual barriers, particularly in educational and urban settings. This paper proposes a multimodal LLM framework that transforms auditory inputs such as noisy speech, accents, or sign language into coherent speech synthesis and written outputs, enabling seamless accessibility. Leveraging transformer-conformer architectures, our system fuses audio spectrograms, lip-reading visuals, and textual context via cross-modal attention mechanisms, achieving superior performance in real-time transcription (WER < 5% on diverse datasets) and voice cloning tailored to user prosody. Key innovations include adaptive noise suppression for hearing aid integration, ethical personalization to preserve speaker identity, and deployment on edge devices for low-latency applications like VR classrooms. Evaluations on benchmarks (e.g., LibriSpeech, VoxCeleb) and user trials with 50 participants (including seniors and hard-of-hearing students) demonstrate 30% improvements in comprehension accuracy and user satisfaction over baselines like Whisper and GPT-4V. By bridging auditory-to-text/speech gaps, this framework advances AI pedagogies for immersive learning, promotes equity in communication, and sets foundations for scalable IoT-enhanced inclusive tools. Future directions explore federated learning for privacy-preserving multilingual expansions.