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HLT '93 Proceedings of the workshop on Human Language Technology
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Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Inclusion of deaf students in computer science classes using real-time speech transcription
Proceedings of the 12th annual SIGCSE conference on Innovation and technology in computer science education
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
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EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
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IEEE Transactions on Audio, Speech, and Language Processing
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Accessibility Evaluation of Classroom Captions
ACM Transactions on Accessible Computing (TACCESS)
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Real-time transcription has been shown to be valuable in facilitating non-native speakers' comprehension in real-time communication. Automated speech recognition (ASR) technology is a critical ingredient for its practical deployment. This paper presents a series of studies investigating how the quality of transcripts generated by an ASR system impacts user comprehension and subjective evaluation. Experiments are first presented comparing performance across three different transcription conditions: no transcript, a perfect transcript, and a transcript with Word Error Rate (WER) =20%. We found 20% WER was the most likely critical point for transcripts to be just acceptable and useful. Then we further examined a lower WER of 10% (a lower bound for today's state-of-the-art systems) employing the same experimental design. The results indicated that at 10% WER comprehension performance was significantly improved compared to the no-transcript condition. Finally, implications for further system development and design are discussed.