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Acquiring speech transcriptions using mismatched crowdsourcing
Preethi Jyothi
,
Mark Hasegawa-Johnson
Electrical and Computer Engineering
Coordinated Science Lab
Speech and Hearing Science
Linguistics
Beckman Institute for Advanced Science and Technology
Siebel School of Computing and Data Science
Social & Behavioral Sciences Institute
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Keyphrases
Hindi
100%
Mismatched Crowdsourcing
100%
Speech Transcription
100%
Crowdsourcing
75%
Native Speaker
50%
Target Language
50%
Repetition Code
50%
Information Theory
25%
Error-free
25%
Noisy Channel
25%
Conditional Entropy
25%
Number of Bits
25%
Transcriber
25%
Error Correction Codes
25%
Auxiliary Information
25%
Speech Recognition System
25%
Speech Corpus
25%
Perceptual Bias
25%
2-bit
25%
Maximum Likelihood Decoding
25%
Mechanical Turk
25%
Decoding Rule
25%
Computer Science
Target Language
100%
Repetition Code
100%
Theoretic Approach
50%
Conditional Entropy
50%
Critical Resource
50%
Speech Recognition System
50%
speech corpus
50%
Mechanical Turk
50%
maximum-likelihood decoding
50%
Auxiliary Information
50%
Crowdsourcing Technique
50%
Error-correcting code
50%
Nursing and Health Professions
Logopedics
100%
Maximum Likelihood Method
33%
Automatic Speech Recognition
33%
Social Sciences
Native Speaker
100%
Speech Recognition
50%