Eliciting and Analysing Users' Envisioned Dialogues with Perfect Voice Assistants

Abstract

We present a dialogue elicitation study to assess how users envision conversations with a perfect voice assistant (VA). In an online survey, N=205 participants were prompted with everyday scenarios, and wrote the lines of both user and VA in dialogues that they imagined as perfect. We analysed the dialogues with text analytics and qualitative analysis, including number of words and turns, social aspects of conversation, implied VA capabilities, and the influence of user personality. The majority envisioned dialogues with a VA that is interactive and not purely functional; it is smart, proactive, and has knowledge about the user. Attitudes diverged regarding the assistant's role as well as it expressing humour and opinions. An exploratory analysis suggested a relationship with personality for these aspects, but correlations were low overall. We discuss implications for research and design of future VAs, underlining the vision of enabling conversational UIs, rather than single command "Q&As".

15 pages, 4 figures, 3 tables, ACM CHI 2021

References in corpus (3)

• What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents
• Developing a Personality Model for Speech-based Conversational Agents Using the Psycholexical Approach
• Mental Workload and Language Production in Non-Native Speaker IPA Interaction

Cited by in corpus (10)

• User Interaction Patterns and Breakdowns in Conversing with LLM-Powered Voice Assistants
• How People Prompt to Create Interactive VR Scenes
• Listening to the Voices: Describing Ethical Caveats of Conversational User Interfaces According to Experts and Frequent Users
• Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts
• Coimagining the Future of Voice Assistants with Cultural Sensitivity
• LLM-Mediated Domain-Specific Voice Agents: The Case of TextileBot
• Wizundry: A Cooperative Wizard of Oz Platform for Simulating Future Speech-based Interfaces with Multiple Wizards
• StepWrite: Adaptive Planning for Speech-Driven Text Generation
• Digital Modeling for Everyone: Exploring How Novices Approach Voice-Based 3D Modeling
• "Mango Mango, How to Let The Lettuce Dry Without A Spinner?": Exploring User Perceptions of Using An LLM-Based Conversational Assistant Toward Cooking Partner