Paper Info
Title
Task Rebalancing: Improving Multilingual Communication with Native Speakers-Generated Highlights on Automated Transcripts.
Abstract
In multilingual communication through a common language among both native speakers (NS) and non-native speakers (NNS), NNS may encounter problems in comprehending the messages of NS or following conversations. Even though automated speech recognition (ASR) transcripts provide support to NNS, such transcripts may contain errors and impose the need to simultaneously listen and read. To reduce this burden, we propose adding another channel (i.e., highlighting) through which NS can help NNS by highlighting the critical parts of transcripts, thus making them more useful to NNS. In a laboratory study involving 14 triads (two NS and one NNS in each triad), participants engaged in collaborative discussions under two conditions: audio conferencing plus ASR transcripts with and without the highlighting function. NS showed various motivations to perform the extra task of highlighting. The highlighting efforts helped NS themselves focus on the discussion and enhanced their task performance while increasing the clarity and comfort perceived by NNS during communication. Having NS generating highlights can benefit both NS and NNS, but in different ways. We discuss the implications for research and design of multilingual collaborative work.
Year
DOI
Venue
2017
10.1145/2998181.2998304
CSCW
Keywords
Field
DocType
Automated speech recognition (ASR), multilingual communication, social annotation, highlighting tools, real-time transcripts
CLARITY,Computer science,Communication channel,Human–computer interaction,Artificial intelligence,Natural language processing
Conference
Citations 
PageRank 
References 
2
0.41
13
Authors
3
Name
Order
Citations
PageRank
Mei-Hua Pan120.74
Naomi Yamashita225127.56
Hao-Chuan Wang329645.80