Voice assistant technology applied to populations with developmental and physical disabilities

Volochtchuk, Ana Vitória Lachowski; Leite, Higor; Vieira, Alessandro Diogo

doi:10.1080/0144929x.2023.2243343

Cited by 3 publications

(2 citation statements)

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“…As established in Section 1, EVAs can improve PwDs' autonomy and well-being (Brewer et al, 2018 ; Volochtchuk et al, 2023 ), so voice assistant accessibility is crucial. There is an abundance of previous literature creating EVAs with dementia-friendly features (see Section 1.2), but they all use off-the-shelf speech processing .…”

Section: Discussionmentioning

confidence: 99%

“…A system, called COACH, was created to assist PwDs when washing their hands by reminding the user using verbal prompts if they forgot any handwashing steps (Mihailidis et al, 2008;Bharucha et al, 2009;König et al, 2017). Later work supports this point further, as caregivers have reported that they found it helpful when a prototype EVA assisted PwDs with repetitive routine tasks and answered questions multiple times (e.g., patiently reciting the weather forecast 15 times in a row; Wolters et al, 2016;Hoy, 2018;Volochtchuk et al, 2023). Research tends to focus on the reduction of pressure on the caregivers, but this is typically due to a wonderful increase in PwDs' daily independence (Brewer et al, 2018).…”

Section: Language Technologies For People With Dementiamentioning

confidence: 99%

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You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification

Addlesee,

Eshghi

2024

Front. Dement.

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In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

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Section: Discussionmentioning

confidence: 99%