Time-Compressing Speech: ASR Transcripts Are an Effective Way to Support Gist Extraction
MLMI '08 Proceedings of the 5th international workshop on Machine Learning for Multimodal Interaction
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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Real-time transcription generated by automated speech recognition (ASR) technologies with a reasonably high accuracy has been demonstrated to be valuable in facilitating non-native speakers' comprehension in real-time communication. Besides errors, time delay often exists due to technical problems in automated transcription as well. This study focuses on how the time delay of transcription impacts non-native speakers' comprehension performance and user experience. The experiment design simulated a one-way computermediated communication scenario, where comprehension performance and user experiences in 3 transcription conditions (no transcript; perfect transcripts with a 2-second delay; and transcripts with a 10% word-error-rate and a 2-second delay) were compared. The results showed that the participants can benefit from the transcription with a 2-second time delay, as their comprehension performance in this condition was improved compared with the no-transcript condition. However, the transcription presented with delay was found to have negative effects on user experience. In the final part of the paper, implications for further system development and design are discussed.