著者

芦川 将之 川村 隆浩 大須賀 昭彦

出版者

一般社団法人 人工知能学会

雑誌

人工知能学会論文誌 (ISSN:13460714)

巻号頁・発行日

vol.29, no.6, pp.503-515, 2014-11-01 (Released:2014-09-25)

参考文献数

25

被引用文献数

2

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Open Crowdsourcing platforms like Amazon Mechanical Turk provide an attractive solution for process of high volume tasks with low costs. However problems of quality control is still of major interest. In this paper, we design a private crowdsourcing system, where we can devise methods for the quality control. For the quality control, we introduce four worker selection methods, each of which we call preprocessing filtering, real-time filtering, post processing filtering, and guess processing filtering. These methods include a novel approach, which utilizes a collaborative filtering technique in addition to a basic approach of initial training or gold standard data. For an use case, we have built a very large dictionary, which is necessary for Large Vocabulary Continuous Speech Recognition and Text-to-Speech. We show how the system yields high quality results for some difficult tasks of word extraction, part-of-speech tagging, and pronunciation prediction to build a large dictionary.