ABSTRACT
User Experience in Human-Computer Interaction is composed of a multitude of building blocks, one of which is how Voice Assistants (VAs) talk to their users. Linguistic considerations around syntax, grammar, and lexis have proven to influence users’ perception of VAs. Users have nuanced preferences regarding how they want their VAs to talk to them. Previous studies have found these preferences to differ between domains, but an exhaustive and methodical overview is still outstanding. By means of an A/B study spanning over domains as well as dialog types, this paper methodically closes this gap and explores the degree of domain-sensitivity across different types of dialogs in German. The results paint a mixed picture regarding the importance of domain-sensitivity. While some degree of domain-sensitivity was found for in-car prompts, it generally seems to play a rather minor role in users’ experience of VAs in the vehicle.
- Ignacio Alvarez, Hanan Alnizami, Jerone Dunbar, Andrea Johnson, France Jackson, and Juan Gilbert. 2011. Designing Driver-Centric Natural Voice User Interfaces. In Adjunct Proceedings of the 3rd International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI'11), November 11-02, 2011, Salzburg, Germany. ACM Inc., New York, NY, 156–159Google Scholar
- Frantisek Danes (Ed.). 2015. Papers on functional sentence perspective, De Gruyter Mouton, BerlinGoogle Scholar
- David L. Strayer, Joanna Turrill, Joel M. Cooper, James R. Coleman, Nathan Medeiros-Ward, and Francesco Biondi. 2015. Assessing Cognitive Distraction in the Automobile. In The Journal of the Human Factors and Ergonomics Society, Volume 57, Issue 8 (November 2015), 1300–1324. DOI: 10.1177/0018720815575149Google ScholarCross Ref
- Luis F. D'Haro, Zoraida Callejas, and Satoshi Nakamura (Eds.). 2021. Conversational Dialogue Systems for the Next Decade, Lecture Notes in Electrical Engineering 704, Springer, SingaporeGoogle Scholar
- DIN EN ISO 9241-110. 2020. Ergonomics of human-system interaction - Part 110: Interaction principles (ISO 9241-110:2020). DOI: 10.31030/3147467Google ScholarCross Ref
- Gabriel Haas, Michael Rietzler, Matt Jones, and Enrico Rukzio. 2022. Keep it Short: A Comparison of Voice Assistants’ Response Behavior. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems (CHI '22), 2022, New Orleans, USA, ACM Inc., New York, NY, 12 pages. DOI: 10.1145/3491102.3517684Google ScholarDigital Library
- Florian Hinterleitner. 2017. Influencing Factors on Perceptual Quality Quality of Synthetic Speech. T-Labs Series in Telecommunication Services, 2017, Springer, Singapore, 69–100. DOI: 10.1007/978-981-10-3734-4_5Google ScholarCross Ref
- Rosalind Horowitz and S. J. Samuels (Eds.). 1987. Comprehending Oral and Written Language: Critical Contrasts for Literacy and Schooling, Academic Press, Cambridge, MA.Google Scholar
- Carolin Kaiser and René Schallner. 2022. The impact of emotional voice assistants on consumers’ shopping attitude and behavior. In Wirtschaftsinformatik 2022, 10 (February 2022), 5 pages. https://aisel.aisnet.org/wi2022/workshops/workshops/10Google Scholar
- Katharina Kühne, Martin H. Fischer, and Yuefang Zhou. 2020. The Human Takes It All: Humanlike Synthesized Voices Are Perceived as Less Eerie and More Likable. Evidence From a Subjective Ratings Study. In Front. Neurorobot., 2020,. DOI: 10.3389/fnbot.2020.593732Google ScholarCross Ref
- Eun-Ju Lee. 2010. The more humanlike, the better? How speech type and users’ cognitive style affect social responses to computers. In Computers in Human Behavior, Volume 26, Issue 4, 2010, 665–672. DOI: 10.1016/j.chb.2010.01.003Google ScholarDigital Library
- Wolfgang Lenhard and Alexandra Lenhard. 2011. Berechnung des Lesbarkeitsindex LIX nach Björnson Psychometrica. DOI: 10.13140/RG.2.1.1512.3447Google ScholarCross Ref
- Gesa A. Linnemann and Regina Jucks. 2018. ‘Can I Trust the Spoken Dialogue System Because It Uses the Same Words as I Do?’—Influence of Lexically Aligned Spoken Dialogue Systems on Trustworthiness and User Satisfaction. In Interacting with Computers, Volume 30, Issue 3, 2018, 173–186. DOI: 10.1093/iwc/iwy005Google ScholarCross Ref
- Conor McGinn and Ilaria Torre. 2019. Can you Tell the Robot by the Voice? An Exploratory Study on the Role of Voice in the Perception of Robots. In 14th ACM/IEEE International Conference on Human-Robot Interaction (HRI), March 11-14, 2019, Daegu, Korea. Institute of Electrical and Electronics Engineers, Inc., 211–221. DOI: 10.1109/HRI.2019.8673305Google ScholarCross Ref
- Anna-Maria Meck, Christoph Draxler, and Thurid Vogt. 2022. A Question of Fidelity: Comparing Different User Testing Methods for Evaluating In-Car Prompts. In Proceedings of the 4th Conference on Conversational User Interfaces (CUI'22), July 26-28, 2022, Glasgow, UK. ACM Inc., New York, NY, 1–5. DOI: 10.1145/3543829.3544519Google ScholarDigital Library
- Anna-Maria Meck and Lisa Precht. 2021. How to Design the Perfect Prompt: A Linguistic Approach to Prompt Design in Automotive Voice Assistants – An Exploratory Study. In Proceedings of the 13th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '21), September 9-14, 2021, Leeds, UK. ACM Inc., New York, NY, 237–246. DOI: 10.1145/3409118.3475144Google ScholarDigital Library
- Anirudh Nallapaneni. 2021. Identifying the Influence of Emotional Voice Style in Proactive Automobile Voice Interfaces. Master's thesis. Eindhoven University of Technology (TU/e), Eindhoven, The NetherlandsGoogle Scholar
- Clifford Nass and Scott Brave. 2007. Wired for Speech, The MIT Press, Cambridge, MA.Google Scholar
- Jakob Nielsen and Rolf Molich. 1990. Heuristic evaluation of user interfaces. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (CHI '90), April 1-5, 1990, Seattle, USA. ACM Inc., New York, NY, 249–256. DOI: 10.1145/97243.97281Google ScholarDigital Library
- Cathy Pearl. 2016. Designing Voice User Interfaces: Principles of Conversational Experiences, O'Reilly Media, Sebastopol, CA.Google Scholar
- RStudio Team. 2022. RStudio: Integrated Development Environment for R, RStudio version 2022.7.1.554Google Scholar
- Maria Schmidt, Daniela Stier, Steffen Werner, and Wolfgang Minker. 2019. Exploration and assessment of proactive use cases for an in-car voice assistant. In Konferenz Elektronische Sprachsignalverarbeitung (ESSV), March 3-8, 2019, Dresden, Germany. Studientexte zur Sprachkommunikation, Tübingen, Germany, 148–155. https://www.essv.de/paper.php?id=76Google Scholar
- Rob Semmens, Nikolas Martelaro, Pushyami Kaveti, Simon. Stent, and Wendy Ju. 2019. Is Now A Good Time?: An Empirical Study of Vehicle-Driver Communication Timing. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems (CHI '19), May 4-9, 2019, Glasgow, UK. ACM Inc., New York, NY, 12 pages. DOI: 10.1145/3290605.3300867Google ScholarDigital Library
- Ben Shneiderman and Catherine Plaisant. 2004. Designing the user interface: Strategies for effective Human-Computer Interaction, Addison Wesley, Boston, MA.Google ScholarDigital Library
- Daniela Stier and Ellen Sigloch. 2019. Linguistic Design of In-Vehicle Prompts in Adaptive Dialog Systems: An Analysis of Potential Factors Involved in the Perception of Naturalness. IN Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization (UMAP ’19), June 9-12, 2019, Lacarna, Cyprus. ACM Inc., New York, NY, 191–195. DOI: 10.1145/3320435.3320469Google ScholarDigital Library
- Sarah T. Völkel, Daniel Buschek, Malin Eiband, Benjamin R. Cowan, and Heinrich Hussmann. 2021. Eliciting and Analysing Users’ Envisioned Dialogues with Perfect Voice Assistants. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI'21), May 8-13, 2021, Yokohama, Japan. ACM Inc., New York, NY, 1–15. DOI: 10.1145/3411764.3445536Google ScholarDigital Library
- Merel C. Wolf, Marloes M. L. Muijselaar, Anne M. Boonstra, and Elise H. de Bree. 2018. The relationship between reading and listening comprehension: shared and modality‑specific components. In Reading and Writing, 32 (December 2018), 1747–1767. DOI: 10.1007/s11145-018-9924-8Google ScholarCross Ref
Index Terms
- Secure, Comfortable or Functional: Exploring Domain-Sensitive Prompt Design for In-Car Voice Assistants
Recommendations
How to Design the Perfect Prompt: A Linguistic Approach to Prompt Design in Automotive Voice Assistants – An Exploratory Study
AutomotiveUI '21: 13th International Conference on Automotive User Interfaces and Interactive Vehicular ApplicationsIn-vehicle voice user interfaces (VUIs) are becoming increasingly popular while needing to handle more and more complex functions. While many guidelines exist in terms of dialog design, a methodical and encompassing approach to prompt design is absent ...
Exploring Humor as a Repair Strategy During Communication Breakdowns with Voice Assistants
CUI '23: Proceedings of the 5th International Conference on Conversational User InterfacesVoice assistants are becoming increasingly useful and support realistic conversations, yet communication breakdowns occur. We investigate the use of humor as a repair strategy in an experiment where the voice assistant makes a mistake and then utilizes ...
Effect of Speech Entrainment in Human-Computer Conversation: A Review
Intelligent Human Computer InteractionAbstractThe phenomenon of entrainment in conversation is the process where participants become more similar to each other in terms of different verbal and non-verbal aspects such as acoustic-prosodic, lexical, syntactic, pitch, and speech rate. This ...
Comments