# Whither the Priors for (Vocal) Interactivity?

arXiv: https://arxiv.org/abs/2203.08578 · HTML: https://mlsanity.com/paper/2203.08578/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 24 references that are in the map.
- First posted on arXiv 16 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Scaling ASR Improves Zero and Few Shot Learning](https://mlsanity.com/paper/2111.05948/) (2021)
- [Spoken Language Interaction with Robots: Research Issues and Recommendations, Report from the NSF Future Directions Workshop](https://mlsanity.com/paper/2011.05533/) (2020)
- [Is spoken language all-or-nothing? Implications for future speech-based human-machine interaction](https://mlsanity.com/paper/1607.05174/) (2016)
- [Vocal Interactivity in Crowds, Flocks and Swarms: Implications for Voice User Interfaces](https://mlsanity.com/paper/1907.11656/) (2019)

## Abstract (arXiv)

Voice-based communication is often cited as one of the most `natural' ways in which humans and robots might interact, and the recent availability of accurate automatic speech recognition and intelligible speech synthesis has enabled researchers to integrate advanced off-the-shelf spoken language technology components into their robot platforms. Despite this, the resulting interactions are anything but `natural'. It transpires that simply giving a robot a voice doesn't mean that a user will know how (or when) to talk to it, and the resulting `conversations' tend to be stilted, one-sided and short. On the surface, these difficulties might appear to be fairly trivial consequences of users' unfamiliarity with robots (and vice versa), and that any problems would be mitigated by long-term use by the human, coupled with `deep learning' by the robot. However, it is argued here that such communication failures are indicative of a deeper malaise: a fundamental lack of basic principles -- priors -- underpinning not only speech-based interaction in particular, but (vocal) interactivity in general. This is evidenced not only by the fact that contemporary spoken language systems already require training data sets that are orders-of-magnitude greater than that experienced by a young child, but also by the lack of design principles for creating effective communicative human-robot interaction. This short position paper identifies some of the key areas where theoretical insights might help overcome these shortfalls.