A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912462213742592 |
|---|---|
| author | Salihs, Sumaya Ahmed Wiafe, Isaac Abdulai, Jamal-Deen Atsakpo, Elikem Doe Ayoka, Gifty Cave, Richard Ekpezu, Akon Obu Holloway, Catherine Tomanek, Katrin Winful, Fiifi Baffoe Payin |
| author_facet | Salihs, Sumaya Ahmed Wiafe, Isaac Abdulai, Jamal-Deen Atsakpo, Elikem Doe Ayoka, Gifty Cave, Richard Ekpezu, Akon Obu Holloway, Catherine Tomanek, Katrin Winful, Fiifi Baffoe Payin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_02428 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages Salihs, Sumaya Ahmed Wiafe, Isaac Abdulai, Jamal-Deen Atsakpo, Elikem Doe Ayoka, Gifty Cave, Richard Ekpezu, Akon Obu Holloway, Catherine Tomanek, Katrin Winful, Fiifi Baffoe Payin Computation and Language 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. |
| title | A Cookbook for Community-driven Data Collection of Impaired Speech in LowResource Languages |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.02428 |