# Speech recognition for medical conversations

arXiv: https://arxiv.org/abs/1711.07274 · HTML: https://mlsanity.com/paper/1711.07274/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 18 of 18)

- [A Review of Speaker Diarization: Recent Advances with Deep Learning](https://mlsanity.com/paper/2101.09624/) (2021)
- [Towards Conversational Diagnostic AI](https://mlsanity.com/paper/2401.05654/) (2024)
- [Translatotron 2: High-quality direct speech-to-speech translation with voice preservation](https://mlsanity.com/paper/2107.08661/) (2021)
- [PriMock57: A Dataset Of Primary Care Mock Consultations](https://mlsanity.com/paper/2204.00333/) (2022)
- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [Understanding Medical Conversations: Rich Transcription, Confidence Scores & Information Extraction](https://mlsanity.com/paper/2104.02219/) (2021)
- [VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain](https://mlsanity.com/paper/2404.05659/) (2024)
- [Real-Time Multimodal Cognitive Assistant for Emergency Medical Services](https://mlsanity.com/paper/2403.06734/) (2024)
- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [Adversarial Attacks on Speech Recognition Systems for Mission-Critical Applications: A Survey](https://mlsanity.com/paper/2202.10594/) (2022)
- [Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech](https://mlsanity.com/paper/2210.13397/) (2022)
- [Using Text Injection to Improve Recognition of Personal Identifiers in Speech](https://mlsanity.com/paper/2308.07393/) (2023)
- [ASR Under the Stethoscope: Evaluating Biases in Clinical Speech Recognition across Indian Languages](https://mlsanity.com/paper/2512.10967/) (2025)
- [Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project](https://mlsanity.com/paper/2309.15869/) (2023)
- [Speech Detection For Child-Clinician Conversations In Danish For Low-Resource In-The-Wild Conditions: A Case Study](https://mlsanity.com/paper/2204.11550/) (2022)
- [Post-decoder Biasing for End-to-End Speech Recognition of Multi-turn Medical Interview](https://mlsanity.com/paper/2403.00370/) (2024)
- [Navigating the Reality Gap: On-Device Continual Adaptation of ASR for Clinical Telephony](https://mlsanity.com/paper/2512.16401/) (2025)
- [A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks](https://mlsanity.com/paper/2605.26747/) (2026)

## Abstract (arXiv)

In this work we explored building automatic speech recognition models for transcribing doctor patient conversation. We collected a large scale dataset of clinical conversations (14,000 hr), designed the task to represent the real word scenario, and explored several alignment approaches to iteratively improve data quality. We explored both CTC and LAS systems for building speech recognition models. The LAS was more resilient to noisy data and CTC required more data clean up. A detailed analysis is provided for understanding the performance for clinical tasks. Our analysis showed the speech recognition models performed well on important medical utterances, while errors occurred in causal conversations. Overall we believe the resulting models can provide reasonable quality in practice.