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Community-Supported Shared Infrastructure in Support of Speech Accessibility.

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Title: Community-Supported Shared Infrastructure in Support of Speech Accessibility.
Authors: Hasegawa-Johnson, Mark1 jhasegaw@illinois.edu, Xiuwen Zheng1, Heejin Kim1, Mendes, Clarion1, Dickinson, Meg1, Hege, Erik1, Zwilling, Chris1, Moore Channell, Marie1, Mattie, Laura1, Hodges, Heather2, Ramig, Lorraine2, Bellard, Mary3, Shebanek, Mike4, Sari, Leda4, Kalgaonkar, Kaustubh4, Frerichs, David5, Bigham, Jeffrey P.6, Findlater, Leah6, Lea, Colin6, Herrlinger, Sarah6
Source: Journal of Speech, Language & Hearing Research. Nov2024, Vol. 67 Issue 11, p4162-4175. 14p.
Subject Terms: *Community support, *Health services accessibility, *Dysarthria, *Assistive technology, *Speech disorders, *Machine learning, *People with disabilities, Automatic speech recognition, Cell phones, Descriptive statistics, Parkinson's disease, Personal computers, Data analysis software
Geographic Terms: Illinois, United States
Abstract: Purpose: 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. Method: 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. Results: 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%. Conclusions: 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. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
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Abstract:Purpose: 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. Method: 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. Results: 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%. Conclusions: 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. [ABSTRACT FROM AUTHOR]
ISSN:10924388
DOI:10.1044/2024_JSLHR-24-00122