# Mapping Perceptions of Humanness in Speech-Based Intelligent Personal   Assistant Interaction

**Authors:** Philip R. Doyle, Justin Edwards, Odile Dumbleton, Leigh Clark,, Benjamin R. Cowan

arXiv: 1907.11585 · 2019-07-30

## TL;DR

This study explores how users perceive humanness in speech-based assistants, revealing it as a multidimensional concept with eight key themes that influence user interaction and expectations.

## Contribution

It provides a detailed mapping of user perceptions of humanness in speech interfaces, highlighting differences from human interaction and informing design considerations.

## Key findings

- Perceptions of humanness are multidimensional with eight key themes.
- Users see speech interfaces as more formal, impersonal, and less authentic than humans.
- The themes can guide future research and design of speech-based assistants.

## Abstract

Humanness is core to speech interface design. Yet little is known about how users conceptualise perceptions of humanness and how people define their interaction with speech interfaces through this. To map these perceptions n=21 participants held dialogues with a human and two speech interface based intelligent personal assistants, and then reflected and compared their experiences using the repertory grid technique. Analysis of the constructs show that perceptions of humanness are multidimensional, focusing on eight key themes: partner knowledge set, interpersonal connection, linguistic content, partner performance and capabilities, conversational interaction, partner identity and role, vocal qualities and behavioral affordances. Through these themes, it is clear that users define the capabilities of speech interfaces differently to humans, seeing them as more formal, fact based, impersonal and less authentic. Based on the findings, we discuss how the themes help to scaffold, categorise and target research and design efforts, considering the appropriateness of emulating humanness.

## Full text

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## References

53 references — full list in the complete paper: https://tomesphere.com/paper/1907.11585/full.md

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Source: https://tomesphere.com/paper/1907.11585