Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
4-2015
Abstract
Language barrier is the primary challenge for effectivecross-lingual conversations. Spoken language translation(SLT) is perceived as a cost-effective alternative to lessaffordable human interpreters, but little research has beendone on how people interact with such technology. Using aprototype translator application, we performed a formativeevaluation to elicit how people interact with the technologyand adapt their conversation style. We conducted two setsof studies with a total of 23 pairs (46 participants).Participants worked on storytelling tasks to simulate naturalconversations with 3 different interface settings. Ourfindings show that collocutors naturally adapt their style ofspeech production and comprehension to compensate forinadequacies in SLT. We conclude the paper with thedesign guidelines that emerged from the analysis.
Keywords
Multilingual communication, Spoken language translation, Automatic speech recognition, Machine translation
Discipline
Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, April 18-23
First Page
3473
Last Page
3482
ISBN
9781450331456
Identifier
10.1145/2702123.2702407
Publisher
ACM
City or Country
New York
Citation
Kotaro HARA and IQBAL, Shamsi T..
Effect of machine translation in interlingual conversation: Lessons from a formative study. (2015). CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, April 18-23. 3473-3482.
Available at: https://ink.library.smu.edu.sg/sis_research/4014
Copyright Owner and License
Publisher
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1145/2702123.2702407