# Development of Hybrid ASR Systems for Low Resource Medical Domain Conversational Telephone Speech

arXiv: https://arxiv.org/abs/2210.13397 · HTML: https://mlsanity.com/paper/2210.13397/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 21 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Conformer: Convolution-augmented Transformer for Speech Recognition](https://mlsanity.com/paper/2005.08100/) (2020)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [RWTH ASR Systems for LibriSpeech: Hybrid vs Attention -- w/o Data Augmentation](https://mlsanity.com/paper/1905.03072/) (2019)
- [Speech recognition for medical conversations](https://mlsanity.com/paper/1711.07274/) (2017)
- [RETURNN: The RWTH Extensible Training framework for Universal Recurrent Neural Networks](https://mlsanity.com/paper/1608.00895/) (2016)
- [Conformer-based Hybrid ASR System for Switchboard Dataset](https://mlsanity.com/paper/2111.03442/) (2021)
- [Improving the Training Recipe for a Robust Conformer-based Hybrid Model](https://mlsanity.com/paper/2206.12955/) (2022)

## Built on by (most cited first, 3 of 3)

- [MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder](https://mlsanity.com/paper/2409.14074/) (2024)
- [Efficient Utilization of Large Pre-Trained Models for Low Resource ASR](https://mlsanity.com/paper/2210.15445/) (2022)
- [Unsupervised Pre-Training for Vietnamese Automatic Speech Recognition in the HYKIST Project](https://mlsanity.com/paper/2309.15869/) (2023)

## Abstract (arXiv)

Language barriers present a great challenge in our increasingly connected and global world. Especially within the medical domain, e.g. hospital or emergency room, communication difficulties and delays may lead to malpractice and non-optimal patient care. In the HYKIST project, we consider patient-physician communication, more specifically between a German-speaking physician and an Arabic- or Vietnamese-speaking patient. Currently, a doctor can call the Triaphon service to get assistance from an interpreter in order to help facilitate communication. The HYKIST goal is to support the usually non-professional bilingual interpreter with an automatic speech translation system to improve patient care and help overcome language barriers. In this work, we present our ASR system development efforts for this conversational telephone speech translation task in the medical domain for two languages pairs, data collection, various acoustic model architectures and dialect-induced difficulties.