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    <titleInfo>
        <title>Incremental Speech Processing for Voice Assistant Accessibility</title>
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        <namePart type="given">Angus</namePart>
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            <title>Proceedings of the 19th Annual Meeting of the Young Reseachers’ Roundtable on Spoken Dialogue Systems</title>
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            <namePart type="given">Vojtech</namePart>
            <namePart type="family">Hudecek</namePart>
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        <name type="personal">
            <namePart type="given">Patricia</namePart>
            <namePart type="family">Schmidtova</namePart>
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            <namePart type="given">Tanvi</namePart>
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        <name type="personal">
            <namePart type="given">Javier</namePart>
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            <namePart type="given">Weronika</namePart>
            <namePart type="family">Sieinska</namePart>
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    <abstract>Speech production is nuanced and unique to every individual, but today’s Spoken Dialogue Systems (SDSs) are trained to use general speech patterns to successfully improve performance on various evaluation metrics. However, these patterns do not apply to certain user groups - often the very people that can benefit the most from SDSs. For example, people with dementia produce more disfluent speech than the general population. The healthcare domain is now a popular setting for spoken dialogue and human-robot interaction research. This trend is similar when observing company behaviour. Charities promote industry voice assistants, the creators are getting HIPAA compliance, and their features sometimes target vulnerable user groups. It is therefore critical to adapt SDSs to be more accessible.</abstract>
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%0 Conference Proceedings
%T Incremental Speech Processing for Voice Assistant Accessibility
%A Addlesee, Angus
%Y Hudecek, Vojtech
%Y Schmidtova, Patricia
%Y Dinkar, Tanvi
%Y Chiyah-Garcia, Javier
%Y Sieinska, Weronika
%S Proceedings of the 19th Annual Meeting of the Young Reseachers’ Roundtable on Spoken Dialogue Systems
%D 2023
%8 September
%I Association for Computational Linguistics
%C Prague, Czechia
%F addlesee-2023-incremental
%X Speech production is nuanced and unique to every individual, but today’s Spoken Dialogue Systems (SDSs) are trained to use general speech patterns to successfully improve performance on various evaluation metrics. However, these patterns do not apply to certain user groups - often the very people that can benefit the most from SDSs. For example, people with dementia produce more disfluent speech than the general population. The healthcare domain is now a popular setting for spoken dialogue and human-robot interaction research. This trend is similar when observing company behaviour. Charities promote industry voice assistants, the creators are getting HIPAA compliance, and their features sometimes target vulnerable user groups. It is therefore critical to adapt SDSs to be more accessible.
%U https://aclanthology.org/2023.yrrsds-1.3
%P 9-11

Markdown (Informal)

[Incremental Speech Processing for Voice Assistant Accessibility](https://aclanthology.org/2023.yrrsds-1.3) (Addlesee, YRRSDS-WS 2023)

• Incremental Speech Processing for Voice Assistant Accessibility (Addlesee, YRRSDS-WS 2023)

ACL

• Angus Addlesee. 2023. Incremental Speech Processing for Voice Assistant Accessibility. In Proceedings of the 19th Annual Meeting of the Young Reseachers' Roundtable on Spoken Dialogue Systems, pages 9–11, Prague, Czechia. Association for Computational Linguistics.