Automated video interview judgment on a large-sized corpus collected online
Automated video interview judgment on a large-sized corpus collected online
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在线收集的大型语料库的自动视频面试判断
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Ehsan Hoque
中科院分区：
文献类型：
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作者：
L. Chen;Ru Zhao;C. W. Leong;B. Lehman;G. Feng;Ehsan Hoque
在筛选潜在员工时，基于在线视频的求职面试正变得非常流行。在这项研究中，我们收集了260名在线工作人员的1891段独白面试视频(时长63小时)的语料库。这些视频是由一家主要评估公司的专家根据个性特征和招聘推荐分数进行注释的。我们提出了一种统一的自动分析方法，该方法利用聚类将连续的音视频分析输出转换为离散的伪词文档，然后应用现代文本分类方法对语音内容、韵律和面部表情进行处理。我们的实验表明，使用受访者所说的话(即口头文本)，我们可以预测他们的性格特征，如开放性、严谨性、外向性、宜人性和情绪稳定性，F测量为0.8或更好，而我们预测招聘推荐分数的F测量为0.6。韵律和面部表情对面试判断的作用有限，需要进一步研究。
Online video-based job interviews are becoming very popular in the screening of potential employees. In this study, we collected a corpus of 1891 monologue job interview videos (63 hours in duration) from 260 online workers. These videos were annotated for personality traits and hiring recommendation score by experts from a major assessment company. We proposed a unified method of automatic analysis that consists of using clustering to convert continuous audio/video analysis output to discrete pseudoword documents, and then applying modern text classification methods to process speech content, prosody and facial expressions. Our experiments showed that using what the interviewees say (i.e., spoken text), we can predict their personality traits such as openness, conscientiousness, extraversion, agreeableness, and emotional stability with an F-measure of 0.8 or better, while we get an F-measure of 0.6 in predicting hiring recommendation score. Prosody and facial expressions added limited usefulness on interview judgments and need further investigation.