# A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages

arXiv: https://arxiv.org/abs/2507.02428 · HTML: https://mlsanity.com/paper/2507.02428/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 23 references that are in the map.
- First posted on arXiv 3 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019)
- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [An analysis of degenerating speech due to progressive dysarthria on ASR performance](https://mlsanity.com/paper/2211.00089/) (2022)
- [Towards a Single ASR Model That Generalizes to Disordered Speech](https://mlsanity.com/paper/2412.19315/) (2024)

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

- [Enabling Automatic Disordered Speech Recognition: An Impaired Speech Dataset in the Akan Language](https://mlsanity.com/paper/2602.05406/) (2026)

## Abstract (arXiv)

This study presents an approach for collecting speech samples to build Automatic Speech Recognition (ASR) models for impaired speech, particularly, low-resource languages. It aims to democratize ASR technology and data collection by developing a "cookbook" of best practices and training for community-driven data collection and ASR model building. As a proof-of-concept, this study curated the first open-source dataset of impaired speech in Akan: a widely spoken indigenous language in Ghana. The study involved participants from diverse backgrounds with speech impairments. The resulting dataset, along with the cookbook and open-source tools, are publicly available to enable researchers and practitioners to create inclusive ASR technologies tailored to the unique needs of speech impaired individuals. In addition, this study presents the initial results of fine-tuning open-source ASR models to better recognize impaired speech in Akan.