# Coimagining the Future of Voice Assistants with Cultural Sensitivity

arXiv: https://arxiv.org/abs/2403.17599 · HTML: https://mlsanity.com/paper/2403.17599/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 97 references that are in the map.
- First posted on arXiv 26 Mar 2024.

## Builds on (most cited first, 11 of 11)

- [VATEX: A Large-Scale, High-Quality Multilingual Dataset for Video-and-Language Research](https://mlsanity.com/paper/1904.03493/) (2019)
- [SLURP: A Spoken Language Understanding Resource Package](https://mlsanity.com/paper/2011.13205/) (2020)
- [Towards Deep Conversational Recommendations](https://mlsanity.com/paper/1812.07617/) (2018)
- [MASSIVE: A 1M-Example Multilingual Natural Language Understanding Dataset with 51 Typologically-Diverse Languages](https://mlsanity.com/paper/2204.08582/) (2022)
- [What Makes a Good Conversation? Challenges in Designing Truly Conversational Agents](https://mlsanity.com/paper/1901.06525/) (2019)
- [The State of Speech in HCI: Trends, Themes and Challenges](https://mlsanity.com/paper/1810.06828/) (2018)
- [Mapping Perceptions of Humanness in Speech-Based Intelligent Personal Assistant Interaction](https://mlsanity.com/paper/1907.11585/) (2019)
- [Eliciting and Analysing Users' Envisioned Dialogues with Perfect Voice Assistants](https://mlsanity.com/paper/2102.13508/) (2021)
- [Not Only WEIRD but "Uncanny"? A Systematic Review of Diversity in Human-Robot Interaction Research](https://mlsanity.com/paper/2304.08750/) (2023)
- [Transcending the "Male Code": Implicit Masculine Biases in NLP Contexts](https://mlsanity.com/paper/2304.12810/) (2023)
- ["I'm" Lost in Translation: Pronoun Missteps in Crowdsourced Data Sets](https://mlsanity.com/paper/2304.13557/) (2023)

## Built on by (most cited first, 3 of 3)

- [Social Identity in Human-Agent Interaction: A Primer](https://mlsanity.com/paper/2508.16609/) (2025)
- [Crossroads of Continents: Automated Artifact Extraction for Cultural Adaptation with Large Multimodal Models](https://mlsanity.com/paper/2407.02067/) (2024)
- [More-than-Human Storytelling: Designing Longitudinal Narrative Engagements with Generative AI](https://mlsanity.com/paper/2505.23780/) (2025)

## Abstract (arXiv)

Voice assistants (VAs) are becoming a feature of our everyday life. Yet, the user experience (UX) is often limited, leading to underuse, disengagement, and abandonment. Co-designing interactions for VAs with potential end-users can be useful. Crowdsourcing this process online and anonymously may add value. However, most work has been done in the English-speaking West on dialogue data sets. We must be sensitive to cultural differences in language, social interactions, and attitudes towards technology. Our aims were to explore the value of co-designing VAs in the non-Western context of Japan and demonstrate the necessity of cultural sensitivity. We conducted an online elicitation study (N = 135) where Americans (n = 64) and Japanese people (n = 71) imagined dialogues (N = 282) and activities (N = 73) with future VAs. We discuss the implications for coimagining interactions with future VAs, offer design guidelines for the Japanese and English-speaking US contexts, and suggest opportunities for cultural plurality in VA design and scholarship.