Multimodal Artificial Intelligence Systems: A Review of Retrieval-Augmented Generation, Voice Processing, and Document Intelligence

Abstract

Artificial Intelligence has evolved rapidly, leading to the development of systems that can process information from multiple sources such as text, speech, images, and documents. Recent advancements in Retrieval-Augmented Generation (RAG), Natural Language Processing (NLP), Speech-to-Text (STT), and Text-to-Speech (TTS) have improved the capabilities of intelligent assistants and information retrieval systems. This review paper presents an overview of multimodal AI systems and examines the technologies that enable efficient document understanding, voice interaction, and automated content generation. Various research studies related to retrieval techniques, language models, speech processing, and document intelligence are analyzed to understand their contributions and limitations. The paper also discusses the applications of these systems in education, research, and professional environments. Finally, current challenges and future opportunities in the development of multimodal AI assistants are highlighted. The review shows that integrating multiple AI technologies into a unified framework can improve accessibility, productivity, and user experience across different domains.