ASR satisficing: the effects of ASR accuracy on speech retrieval
Abstract
We examine how differences in the accuracy of Automatic Speech Recognition transcripts affect users' ability to use these in tasks requiring the retrieval of speech "documents". We compare performance measures, processing strategies, and preference data for subjects using transcripts and speech data to perform a series of relevance judgment and summary tasks on transcripts with different levels of accuracy. Results show effects for transcript quality on solution accuracy, time to solution, amount of speech played for the task, likelihood of subjects abandoning use of a transcript, and subject perceptions of task difficulty, transcript utility, readability, and comprehensibility. 1. INTRODUCTION Research on Automatic Speech Recognition (ASR) generally assumes perfect transcription accuracy to be its holy grail. The more accurately a system can transcribe an utterance, the better that system performs (modulo time factors) Recently this assumption is being challenged, however. Different...
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Revealing algorithmic errors can undermine users' perceptions of accuracy.
“We also confirm other work showing that revealing algorithmic errors can undermine users' perceptions of accuracy [50,51,60,81].”
From Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information? (2020) · cites this paper as [81]
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Progressive Disclosure: When, Why, and How Do Users Want Algorithmic Transparency Information?
Springer, A., & Whittaker, S. (2020) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3374218 · Cited by 84 · see citing works
Self-citationError correction of voicemail transcripts in SCANMail
Burke, M., Amento, B., & Isenhour, P. (2006) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems · doi:10.1145/1124772.1124823 · Cited by 21 · see citing works
Measuring the acceptable word error rate of machine-generated webcast transcripts
Munteanu, C., Penn, G., Baecker, R., Toms, E., & James, D. (2006) · Interspeech 2006 · doi:10.21437/interspeech.2006-40 · Cited by 21 · see citing works
- Authors
- Stark, L., Whittaker, S., & Hirschberg, J.
- Venue
- 6th International Conference on Spoken Language Processing (ICSLP 2000)
- Publisher
- ISCA
- Published
- 2000-10-16
- Pages
- vol. 3, 1069-1072-0
- OpenAlex
- W164174688
- Cited by
- 26
- References
- 0
- Citations by year
- 2017: 3 · 2012: 1
- FWCI
- 1.11
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Spoken Language Processing (ICSLP) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Keywords
- speech recognition, transcription accuracy, speech retrieval, relevance assessment, user preferences, task abandonment, comprehensibility, task difficulty
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