Does Confidence Reporting from the Crowd Benefit Crowdsourcing Performance?

We explore the design of an effective crowdsourcing system for an $M$-ary classification task. Crowd workers complete simple binary microtasks whose results are aggregated to give the final classification decision. We consider the scenario where the workers have a reject option so that they are allowed to skip microtasks when they are unable to or choose not to respond to binary microtasks. Additionally, the workers report quantized confidence levels when they are able to submit definitive answers. We present an aggregation approach using a weighted majority voting rule, where each worker's response is assigned an optimized weight to maximize crowd's classification performance. We obtain a couterintuitive result that the classification performance does not benefit from workers reporting quantized confidence. Therefore, the crowdsourcing system designer should employ the reject option without requiring confidence reporting.Comment: 6 pages, 4 figures, SocialSens 2017. arXiv admin note: text overlap with arXiv:1602.0057

Mismatched Crowdsourcing based Language Perception for Under-resourced Languages

AbstractMismatched crowdsourcing is a technique for acquiring automatic speech recognizer training data in under-resourced languages by decoding the transcriptions of workers who don’t know the target language using a noisy-channel model of cross-language speech perception. All previous mismatched crowdsourcing studies have used English transcribers; this study is the first to recruit transcribers with a different native language, in this case, Mandarin Chinese. Using these data we are able to compute statistical models of cross-language perception of the tones and phonemes from transcribers based on phone distinctive features and tone features. By analyzing the phonetic and tonal variation mappings and coverages compared with the dictionary of the target language, we evaluate the different native languages’ effect on the transcribers’ performances