Year: 2024
Type: article
Abstract: Automatic speech recognition (ASR) and retrieval-augmented generation (RAG) systems have seen remarkable progress in handling multilingualism, noise robustness, real-time transcription, and knowledge-intensive tasks. The survey reviews 12 key papers that contribute to advancements in ASR and RAG, covering approaches like end-to-end multilingual models, noise-reduction techniques, and real-time speech processing. It also examines RAG systems that enhance generative models by integrating retrieval mechanisms for improved accuracy in tasks like question answering and summarization. By categorizing the papers into themes, this survey highlights key methodologies, compares their performance, and identifies future directions for improving ASR and RAG technologies in handling real-world challenges.
Authors: Edwin Alex Shaji, Jerishab M Jerishab M, Leya Thomas, M Viraj Prabhu, Asst.Prof Chinchu M Pillai
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405418384