ArticleJournal of speech, language, and hearing research : JSLHR2024
Community-Supported Shared Infrastructure in Support of Speech Accessibility.
Article in Journal of speech, language, and hearing research : JSLHR, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- HeyJay! A corpus of atypical speech for spoken language understanding and automatic speech recognition.Scientific data · 2026Article
- An End-to-End Overview of Clinical Speech AI.IEEE transactions on audio, speech, and language processing (2025) · 2026Article
- ParkMAE: a cross-linguistic masked autoencoder framework for robust Parkinson's disease detection from speech.Scientific reports · 2025Article
- A Review of Clinical Trials in Down Syndrome.International review of research in developmental disabilities · 2025Article
- Artificial Intelligence in Communication Sciences and Disorders: Introduction to the Forum.Journal of speech, language, and hearing research : JSLHR · 2024Article
Corrections and comments
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Authors and funding
25 authors.
Funding
Abstract
purposeThe 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.
methodThe 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.
resultsThe 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%.
conclusionsPreliminary 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. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.27078079.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.