Statistical considerations for crowdsourced perceptual ratings of human speech productions.
Statistical considerations for crowdsourced perceptual ratings of human speech productions.
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对人类语音作品的众包感知评级的统计考虑。
DOI：
10.1080/02664763.2018.1547692
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发表时间：
2019
影响因子：
1.5
通讯作者：
McAllister,Tara
中科院分区：
文献类型：
--
作者：
Fernández,Daniel;Harel,Daphna;Ipeirotis,Panos;McAllister,Tara
自2008年引入学术领域以来，众包已成为学术研究的主要工具。然而，与传统实验室环境不同，众包平台上的工作人员完成任务的条件几乎不可能控制。在沟通障碍的研究中，众包为收集人类言语产生的感知评级提供了一种新颖的解决方案。这样的评级使研究人员能够衡量一种治疗方法是否能够对人类听众对混乱言语的感知产生有意义的改变。本文将探讨众包数据的一些统计考虑因素，特别关注收集人类语音作品的感知评级。随机效应模型应用于以连续和二元方式收集的众包感知评级。进行了模拟研究，以测试所提出的模型在不同数量的工人和任务下的可靠性。最后，将该方法应用于沟通障碍研究的数据集。
Crowdsourcing has become a major tool for scholarly research since its introduction to the academic sphere in 2008. However, unlike in traditional laboratory settings, it is nearly impossible to control the conditions under which workers on crowdsourcing platforms complete tasks. In the study of communication disorders, crowdsourcing has provided a novel solution to the collection of perceptual ratings of human speech production. Such ratings allow researchers to gauge whether a treatment yields meaningful change in how human listeners' perceive disordered speech. This paper will explore some statistical considerations of crowdsourced data with specific focus on collecting perceptual ratings of human speech productions. Random effects models are applied to crowdsourced perceptual ratings collected in both a continuous and binary fashion. A simulation study is conducted to test the reliability of the proposed models under differing numbers of workers and tasks. Finally, this methodology is applied to a data set from the study of communication disorders.