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Retrieval Augmented Generation based context discovery for ASR

2025-09-23 · Dimitrios Siskos, Stavros Papadopoulos, Pablo Peso Parada, Jisi Zhang, Karthikeyan Saravanan, Anastasios Drosou arxiv

This work investigates retrieval augmented generation as an efficient strategy for automatic context discovery in context-aware Automatic Speech Recognition (ASR) system, in order to improve transcription accuracy in the presence of rare or out-of-vocabulary terms. However, identifying the right context automatically remains an open challenge. This work proposes an efficient embedding-based retrieval approach for automatic context discovery in ASR. To contextualize its effectiveness, two alternatives based on large language models (LLMs) are also evaluated: (1) large language model (LLM)-based context generation via prompting, and (2) post-recognition transcript correction using LLMs. Experiments on the TED-LIUMv3, Earnings21 and SPGISpeech demonstrate that the proposed approach reduces WER by up to 17% (percentage difference) relative to using no-context, while the oracle context results in a reduction of up to 24.1%.

📄 PDF Abstract BibTeX arXiv:2509.19567

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Tasks

Speech Recognition

Biomedical Hypothesis Explainability with Graph-Based Context Retrieval

2025-09-15 · Ilya Tyagin, Saeideh Valipour, Aliaksandra Sikirzhytskaya, Michael Shtutman 외 arxiv

We introduce an explainability method for biomedical hypothesis generation systems, built on top of the novel Hypothesis Generation Context Retriever framework. Our approach combines semantic graph-based retrieval and re…

LLM-Driven Discovery of High-Entropy Catalysts via Retrieval-Augmented Generation

2026-03-16 · AI Scientists, Xinyi Lin, Danqing Yin, Ying Guo arxiv

CO2 reduction requires efficient catalysts, yet materials discovery remains bottlenecked by 10-20 year development cycles requiring deep domain expertise. This paper demonstrates how large language models can assist the …

Computational Efficiency

RAG-MCP: Mitigating Prompt Bloat in LLM Tool Selection via Retrieval-Augmented Generation

2025-05-06 · Tiantian Gan, Qiyao Sun

Large language models (LLMs) struggle to effectively utilize a growing number of external tools, such as those defined by the Model Context Protocol (MCP)\cite{IntroducingMCP}, due to prompt bloat and selection complexit…

Decision Making RAG Retrieval Retrieval-augmented Generation+1

Accelerating Manufacturing Scale-Up from Material Discovery Using Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering Schematics Design

2024-12-08 · Sakhinana Sagar Srinivas, Akash Das, Shivam Gupta, Venkataramana Runkana

Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams rem…

Knowledge Graphs Open-Domain Question Answering Question Answering RAG+2

LightRAG: Simple and Fast Retrieval-Augmented Generation

2024-10-08 · Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao 외

Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge sources, enabling more accurate and contextually relevant responses tailored to user needs. However, exi…

Information Retrieval RAG Retrieval Retrieval-augmented Generation