Automated Analysis and Prediction of Job Interview Performance
Automated Analysis and Prediction of Job Interview Performance
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DOI：
10.1109/taffc.2016.2614299
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发表时间：
2018-04-01
影响因子：
11.2
通讯作者：
Hoque，Mohammed Ehsan
中科院分区：
文献类型：
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作者：
Naim，Iftekhar;Tanveer，Md. Iftekhar;Hoque，Mohammed Ehsan
我们提出了一个计算框架，自动量化的工作面试中的语言和非语言行为。该框架是通过分析马萨诸塞州学院69名实习本科生的138次面试视频来训练的。我们的自动分析包括面部表情（例如，微笑、头部姿势、面部跟踪点），语言（例如，单词计数，主题建模），以及韵律信息（例如，音高、语调和停顿）。地面实况标签是通过对9名独立法官的评分进行加权平均而得出的。我们的框架可以自动预测面试特征的评分，如兴奋，友好和参与度，相关系数为0.70或更高，并且可以量化韵律，语言和面部表情的相对重要性。通过分析回归模型学习的相对特征权重，我们的框架建议说得更流利，使用更少的填充词，用“我们”（而不是“我”）说话，使用更多独特的词，更多地微笑。我们还发现，在回答第一个面试问题时被评为高的学生总体上也被评为高（即，第一印象很重要）。最后，我们的MIT Interview数据集可供其他研究人员进一步验证和扩展我们的发现。
We present a computational framework for automatically quantifying verbal and nonverbal behaviors in the context of job interviews. The proposed framework is trained by analyzing the videos of 138 interview sessions with 69 internship-seeking undergraduates at the Massachusetts Institute of Technology (MIT). Our automated analysis includes facial expressions (e.g., smiles, head gestures, facial tracking points), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, and pauses) of the interviewees. The ground truth labels are derived by taking a weighted average over the ratings of nine independent judges. Our framework can automatically predict the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.70 or higher, and can quantify the relative importance of prosody, language, and facial expressions. By analyzing the relative feature weights learned by the regression models, our framework recommends to speak more fluently, use fewer filler words, speak as “we” (versus “I”), use more unique words, and smile more. We also find that the students who were rated highly while answering the first interview question were also rated highly overall (i.e., first impression matters). Finally, our MIT Interview dataset is available to other researchers to further validate and expand our findings.