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Building Proactive Voice Assistants: When and How (not) to Interact

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arxiv 2005.01322 v1 pith:VDSBY33A submitted 2020-05-04 cs.HC

classification cs.HC
keywords userinteractionsproactiverightvoicewhenassistantsdevice
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Voice assistants have recently achieved remarkable commercial success. However, the current generation of these devices is typically capable of only reactive interactions. In other words, interactions have to be initiated by the user, which somewhat limits their usability and user experience. We propose, that the next generation of such devices should be able to proactively provide the right information in the right way at the right time, without being prompted by the user. However, achieving this is not straightforward, since there is the danger it could interrupt what the user is doing too much, resulting in it being distracting or even annoying. Furthermore, it could unwittingly, reveal sensitive/private information to third parties. In this report, we discuss the challenges of developing proactively initiated interactions, and suggest a framework for when it is appropriate for the device to intervene. To validate our design assumptions, we describe firstly, how we built a functioning prototype and secondly, a user study that was conducted to assess users' reactions and reflections when in the presence of a proactive voice assistant. This pre-print summarises the state, ideas and progress towards a proactive device as of autumn 2018.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Chronocept: Instilling a Sense of Time in Machines

    cs.CL 2025-05 conditional novelty 6.0 of 10

    Chronocept is a benchmark that models the temporal validity of statements as skew-normal probability curves over logarithmic time, with baseline regressors predicting the curve parameters.

  2. LLAMAPIE: Proactive In-Ear Conversation Assistants

    cs.LG 2025-05 conditional novelty 6.0 of 10

    A two-model, on-device in-ear assistant that decides when to whisper one to three words of guidance matches a reactive chatbot's accuracy in live interviews while cutting response latency and perceived disruption by m...

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