crowdsourcing speech data science and ai
TRANSCRIPT
Daniela Braga, PhDCEO
DefinedCrowd: Crowdsourcing,
Speech Data Science, AI
Crowdsourcing Week, June 15th 2017
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Reason #1: machines need high quality data to learn
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Reason #2: big data opportunity
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Reason #2: big data
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Reason #3: paradigm shift when teaching machines
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DEMO
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The challenges of crowdsourcing NLP data
Crowd quality Data quality
• Language tests• Job specific tests• Real Time Audits• Built-in language/spam
validators
• Referral system• System of tokens• Legal/privacy compliance
(under NDA)
Quality gateways
Controlled crowd
• Checking for suspicious crowd behavior (multiple accounts creation, peaks of activity, specific job spam, IP check against country of living)
Machine Learning
Data quality control
• Validation steps• Inter-annotator
agreements• Precision and Recall
metrics
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DefinedCrowd combines the best of professional services with SaaS companies
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