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Language coverage for mismatched crowdsourcing
Lav R. Varshney
, Preethi Jyothi
,
Mark Hasegawa-Johnson
Coordinated Science Lab
Beckman Institute for Advanced Science and Technology
Electrical and Computer Engineering
Speech and Hearing Science
Linguistics
Siebel School of Computing and Data Science
Center for Social & Behavioral Science
Research output
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Chapter in Book/Report/Conference proceeding
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Conference contribution
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Keyphrases
Mismatched Crowdsourcing
100%
Native English Speakers
66%
Crowd Worker
66%
Modeling Approach
33%
Non-native
33%
Noisy Data
33%
Information Theory
33%
High Probability
33%
Native Language
33%
Noisy Channel
33%
Native Speaker
33%
Mandarin Speakers
33%
Theory Framework
33%
Phoneme
33%
Misperception
33%
Transcriber
33%
Human Language
33%
Phonological Similarity
33%
Speech Recognition Technology
33%
Language Background
33%
Weighted Set Covering Problem
33%
Computer Science
Speech Recognition
100%
Information Theory
100%
Theoretic Framework
100%
Native Language
100%
Human Language
100%
English Speaker
100%
Phonological Similarity
100%
Phonological Property
100%