Limitations in Speech Recognition for Young Adults with Down Syndrome

Limitations in Speech Recognition for Young Adults with Down Syndrome | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Limitations in Speech Recognition for Young Adults with Down Syndrome Franceli L. Cibrian, Yingchen 'Yuki' Chen, Kayla Anderson, Cecilia Marie Abrahamsson, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3974634/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 15 Feb, 2025 Read the published version in Universal Access in the Information Society → Version 1 posted 11 You are reading this latest preprint version Abstract Speech recognition has the potential to make technology more accessible to users. However, the accuracy of speech recognition remains limited for users with disabilities, including those with Down Syndrome, and the types and frequencies of recognition errors are poorly understood. This paper characterizes these problems, focusing on errors occurring when recognizing Down Syndrome speech. We analyze the transcripts from six speech recognition algorithms (Google, IBM, Otter.ai, Microsoft, AssemblyAI, OpenAI) using the audio content of 15 individuals with Down Syndrome (331 dialogues; 3,428 words). Our analysis shows (1) significant difference in speech recognition accuracy for people with Down Syndrome compared to neurotypical users; (2) the best algorithm for recognizing Down Syndrome speech is OpenAI (Word Accuracy = 67%; F1-score = 0.944), and (3) there is a prevalence of deletion errors followed by substitutions and insertions. These findings have implications for enhancing speech recognition for the next-generation voice assistants to meet the needs of users with Down Syndrome. speech recognition natural language smart home devices neurodiverse users Down Syndrome Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 15 Feb, 2025 Read the published version in Universal Access in the Information Society → Version 1 posted Editorial decision: Revision requested 16 Dec, 2024 Reviews received at journal 16 Dec, 2024 Reviewers agreed at journal 13 Dec, 2024 Reviewers agreed at journal 18 Oct, 2024 Reviews received at journal 14 Mar, 2024 Reviewers agreed at journal 05 Mar, 2024 Reviewers agreed at journal 04 Mar, 2024 Reviewers invited by journal 04 Mar, 2024 Editor assigned by journal 04 Mar, 2024 Submission checks completed at journal 24 Feb, 2024 First submitted to journal 21 Feb, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3974634","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":274634028,"identity":"3ca5d833-a3de-4f8f-94c8-3a29afd10796","order_by":0,"name":"Franceli L. 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