Speech accessibility project (Hasegawa-Johnson et al., 2024)
Creators: name: Hasegawa-Johnson, Mark
name: Zheng, Xiuwen
name: Kim, Heejin
name: Mendes, Clarion
name: Dickinson, Meg
name: Hege, Erik
name: Zwilling, Chris
name: Channell, Marie Moore
name: Mattie, Laura
name: Hodges, Heather
name: Ramig, Lorraine
name: Bellard, Mary
name: Shebanek, Mike
name: Sarι, Leda
name: Kalgaonkar, Kaustubh
name: Frerichs, David
name: P. Bigham, Jeffrey
name: Findlater, Leah
name: Lea, Colin
name: Herrlinger, Sarah
name: Korn, Peter
name: Abou-Zahra, Shadi
name: Heywood, Rus
name: Tomanek, Katrin
name: MacDonald, Bob
Publisher: name: ASHA journals
Year: 2024
Subjects: classificationCode: 420110
subject: Speech pathology
classificationCode: 420318
subject: People with disability
classificationCode: 470102
subject: Communication technology and digital media studies

description: <b>Purpose:</b> The Speech Accessibility Project (SAP) intends to facilitate research and development in automatic speech recognition (ASR) and other machine learning tasks for people with speech disabilities. The purpose of this article is to introduce this project as a resource for researchers, including baseline analysis of the first released data package.<b>Method:</b> The project aims to facilitate ASR research by collecting, curating, and distributing transcribed U.S. English speech from people with speech and/or language disabilities. Participants record speech from their place of residence by connecting their personal computer, cell phone, and assistive devices, if needed, to the SAP web portal. All samples are manually transcribed, and 30 per participant are annotated using differential diagnostic pattern dimensions. For purposes of ASR experiments, the participants have been randomly assigned to a training set, a development set for controlled testing of a trained ASR, and a test set to evaluate ASR error rate.<b>Results: </b>The SAP 2023-10-05 Data Package contains the speech of 211 people with dysarthria as a correlate of Parkinson’s disease, and the associated test set contains 42 additional speakers. A baseline ASR, with a word error rate of 3.4% for typical speakers, transcribes test speech with a word error rate of 36.3%. Fine-tuning reduces the word error rate to 23.7%.<b>Conclusions:</b> Preliminary findings suggest that a large corpus of dysarthric and dysphonic speech has the potential to significantly improve speech technology for people with disabilities. By providing these data to researchers, the SAP intends to significantly accelerate research into accessible speech technology.<b>Supplemental Material S1.</b> Examples of participant responses to the prompt "Please explain the steps to making breakfast for 4 people."Hasegawa-Johnson, M., Zheng, X., Kim, H., Mendes, C., Dickinson, M., Hege, E., Zwilling, C., Channell, M. M., Mattie, L., Hodges, H., Ramig, L., Bellard, M., Shebanek, M., Sarι, L., Kalgaonkar, K., Frerichs, D., Bigham, J. P., Findlater, L., Lea, C., Herrlinger, S., Korn, P., Abou-Zahra, S., Heywood, R., Tomanek, K., &amp; MacDonald, B. (2024). Community-supported shared infrastructure in support of speech accessibility. <i>Journal of Speech, Language, and Hearing Research</i><i>,</i><i> 67</i>(11), 4162–4175. https://doi.org/10.1044/2024_JSLHR-24-00122<b>Publisher Note:</b> This article is part of the Forum: Research Symposium on Artificial Intelligence.