# MedSpeak: A Knowledge Graph-Aided ASR Error Correction Framework for Spoken Medical QA

arXiv: https://arxiv.org/abs/2602.00981 · HTML: https://mlsanity.com/paper/2602.00981/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 15 references that are in the map.
- First posted on arXiv 1 Feb 2026.

## Builds on (most cited first, 11 of 11)

- [MedRAG: Enhancing Retrieval-augmented Generation with Knowledge Graph-Elicited Reasoning for Healthcare Copilot](https://mlsanity.com/paper/2502.04413/) (2025)
- [Retrieval-Augmented Text-to-Audio Generation](https://mlsanity.com/paper/2309.08051/) (2023)
- [Retrieval Augmented End-to-End Spoken Dialog Models](https://mlsanity.com/paper/2402.01828/) (2024)
- [Speech Retrieval-Augmented Generation without Automatic Speech Recognition](https://mlsanity.com/paper/2412.16500/) (2024)
- [Retrieval Augmented Correction of Named Entity Speech Recognition Errors](https://mlsanity.com/paper/2409.06062/) (2024)
- [Contextualization of ASR with LLM using phonetic retrieval-based augmentation](https://mlsanity.com/paper/2409.15353/) (2024)
- [Retrieval Augmented Generation in Prompt-based Text-to-Speech Synthesis with Context-Aware Contrastive Language-Audio Pretraining](https://mlsanity.com/paper/2406.03714/) (2024)
- [GEC-RAG: Improving Generative Error Correction via Retrieval-Augmented Generation for Automatic Speech Recognition Systems](https://mlsanity.com/paper/2501.10734/) (2025)
- [Retrieval-Augmented Dialogue Knowledge Aggregation for Expressive Conversational Speech Synthesis](https://mlsanity.com/paper/2501.06467/) (2025)
- [SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation](https://mlsanity.com/paper/2502.02603/) (2025)
- [Zero-Shot End-To-End Spoken Question Answering In Medical Domain](https://mlsanity.com/paper/2406.05876/) (2024)

## Built on by (most cited first, 1 of 1)

- [Error-Aware TF-IDF Retrieval-Augmented Generation for ASR Error Correction](https://mlsanity.com/paper/2606.24915/) (2026)

## Abstract (arXiv)

Spoken question-answering (SQA) systems relying on automatic speech recognition (ASR) often struggle with accurately recognizing medical terminology. To this end, we propose MedSpeak, a novel knowledge graph-aided ASR error correction framework that refines noisy transcripts and improves downstream answer prediction by leveraging both semantic relationships and phonetic information encoded in a medical knowledge graph, together with the reasoning power of LLMs. Comprehensive experimental results on benchmarks demonstrate that MedSpeak significantly improves the accuracy of medical term recognition and overall medical SQA performance, establishing MedSpeak as a state-of-the-art solution for medical SQA. The code is available at https://github.com/RainieLLM/MedSpeak.