# Inclusivity of AI Speech in Healthcare: A Decade Look Back

arXiv: https://arxiv.org/abs/2505.10596 · HTML: https://mlsanity.com/paper/2505.10596/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 51 references that are in the map.
- First posted on arXiv 15 May 2025.

## Builds on (most cited first, 30 of 33)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech](https://mlsanity.com/paper/1904.02882/) (2019)
- [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021)
- [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021)
- [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017)
- [AISHELL-2: Transforming Mandarin ASR Research Into Industrial Scale](https://mlsanity.com/paper/1808.10583/) (2018)
- [Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debates](https://mlsanity.com/paper/1911.03167/) (2019)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [CVSS Corpus and Massively Multilingual Speech-to-Speech Translation](https://mlsanity.com/paper/2201.03713/) (2022)
- [AISHELL-4: An Open Source Dataset for Speech Enhancement, Separation, Recognition and Speaker Diarization in Conference Scenario](https://mlsanity.com/paper/2104.03603/) (2021)
- [CoVoST: A Diverse Multilingual Speech-To-Text Translation Corpus](https://mlsanity.com/paper/2002.01320/) (2020)
- [THCHS-30 : A Free Chinese Speech Corpus](https://mlsanity.com/paper/1512.01882/) (2015)
- [Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset](https://mlsanity.com/paper/2203.16844/) (2022)
- [NusaCrowd: Open Source Initiative for Indonesian NLP Resources](https://mlsanity.com/paper/2212.09648/) (2022)
- [The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR](https://mlsanity.com/paper/2303.18110/) (2023)
- [EasyCom: An Augmented Reality Dataset to Support Algorithms for Easy Communication in Noisy Environments](https://mlsanity.com/paper/2107.04174/) (2021)
- [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018)
- [MaSS: A Large and Clean Multilingual Corpus of Sentence-aligned Spoken Utterances Extracted from the Bible](https://mlsanity.com/paper/1907.12895/) (2019)
- [EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model](https://mlsanity.com/paper/2106.09317/) (2021)
- [MediaSpeech: Multilanguage ASR Benchmark and Dataset](https://mlsanity.com/paper/2103.16193/) (2021)
- [ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers](https://mlsanity.com/paper/2004.09367/) (2020)
- [KazakhTTS: An Open-Source Kazakh Text-to-Speech Synthesis Dataset](https://mlsanity.com/paper/2104.08459/) (2021)
- [ADIMA: Abuse Detection In Multilingual Audio](https://mlsanity.com/paper/2202.07991/) (2022)
- [RyanSpeech: A Corpus for Conversational Text-to-Speech Synthesis](https://mlsanity.com/paper/2106.08468/) (2021)
- [CrowdSpeech and VoxDIY: Benchmark Datasets for Crowdsourced Audio Transcription](https://mlsanity.com/paper/2107.01091/) (2021)
- [VietMed: A Dataset and Benchmark for Automatic Speech Recognition of Vietnamese in the Medical Domain](https://mlsanity.com/paper/2404.05659/) (2024)
- [The SLT 2021 children speech recognition challenge: Open datasets, rules and baselines](https://mlsanity.com/paper/2011.06724/) (2020)
- [LibriVoxDeEn: A Corpus for German-to-English Speech Translation and German Speech Recognition](https://mlsanity.com/paper/1910.07924/) (2019)
- [OLR 2021 Challenge: Datasets, Rules and Baselines](https://mlsanity.com/paper/2107.11113/) (2021)
- [Kurdish (Sorani) Speech to Text: Presenting an Experimental Dataset](https://mlsanity.com/paper/1911.13087/) (2019)

## Built on by (most cited first, 2 of 2)

- [Unheard in the Digital Age: Rethinking AI Bias and Speech Diversity](https://mlsanity.com/paper/2601.18641/) (2026)
- [Task-Lens: Cross-Task Utility Based Speech Dataset Profiling for Low-Resource Indian Languages](https://mlsanity.com/paper/2602.23388/) (2026)

## Abstract (arXiv)

The integration of AI speech recognition technologies into healthcare has the potential to revolutionize clinical workflows and patient-provider communication. However, this study reveals significant gaps in inclusivity, with datasets and research disproportionately favouring high-resource languages, standardized accents, and narrow demographic groups. These biases risk perpetuating healthcare disparities, as AI systems may misinterpret speech from marginalized groups. This paper highlights the urgent need for inclusive dataset design, bias mitigation research, and policy frameworks to ensure equitable access to AI speech technologies in healthcare.