# Performant ASR Models for Medical Entities in Accented Speech

arXiv: https://arxiv.org/abs/2406.12387 · HTML: https://mlsanity.com/paper/2406.12387/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 26 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020)
- [HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units](https://mlsanity.com/paper/2106.07447/) (2021)
- [WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing](https://mlsanity.com/paper/2110.13900/) (2021)
- [XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale](https://mlsanity.com/paper/2111.09296/) (2021)
- [AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR](https://mlsanity.com/paper/2310.00274/) (2023)
- [The Sound of Healthcare: Improving Medical Transcription ASR Accuracy with Large Language Models](https://mlsanity.com/paper/2402.07658/) (2024)
- [Comprehend Medical: a Named Entity Recognition and Relationship Extraction Web Service](https://mlsanity.com/paper/1910.07419/) (2019)
- [AfriNames: Most ASR models "butcher" African Names](https://mlsanity.com/paper/2306.00253/) (2023)
- [Clinical Named Entity Recognition using Contextualized Token Representations](https://mlsanity.com/paper/2106.12608/) (2021)

## Built on by (most cited first, 7 of 7)

- [Lost in Transcription, Found in Distribution Shift: Demystifying Hallucination in Speech Foundation Models](https://mlsanity.com/paper/2502.12414/) (2025)
- [Afrispeech-Dialog: A Benchmark Dataset for Spontaneous English Conversations in Healthcare and Beyond](https://mlsanity.com/paper/2502.03945/) (2025)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [Automatic Speech Recognition for African Low-Resource Languages: Challenges and Future Directions](https://mlsanity.com/paper/2505.11690/) (2025)
- [Automatic Speech Recognition (ASR) for African Low-Resource Languages: A Systematic Literature Review](https://mlsanity.com/paper/2510.01145/) (2025)
- [MedWER: A Reproducible, Model-Free Evaluation Protocol for Medical Speech Recognition](https://mlsanity.com/paper/2609.05728/) (2026)
- [Benchmarking and Domain Adaptation of Automatic Speech Recognition (ASR) for Adolescent Health Communication in Ghanaian Languages](https://mlsanity.com/paper/2609.29798/) (2026)

## Abstract (arXiv)

Recent strides in automatic speech recognition (ASR) have accelerated their application in the medical domain where their performance on accented medical named entities (NE) such as drug names, diagnoses, and lab results, is largely unknown. We rigorously evaluate multiple ASR models on a clinical English dataset of 93 African accents. Our analysis reveals that despite some models achieving low overall word error rates (WER), errors in clinical entities are higher, potentially posing substantial risks to patient safety. To empirically demonstrate this, we extract clinical entities from transcripts, develop a novel algorithm to align ASR predictions with these entities, and compute medical NE Recall, medical WER, and character error rate. Our results show that fine-tuning on accented clinical speech improves medical WER by a wide margin (25-34 % relative), improving their practical applicability in healthcare environments.