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首页> 外文会议>IEEE International Conference and Workshops on Automatic Face and Gesture Recognition >Automated prediction and analysis of job interview performance: The role of what you say and how you say it
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Automated prediction and analysis of job interview performance: The role of what you say and how you say it

机译：自动预测和求职表现的分析：你所说的和你怎么说的作用

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摘要

Ever wondered why you have been rejected from a job despite being a qualified candidate? What went wrong? In this paper, we provide a computational framework to quantify human behavior in the context of job interviews. We build a model by analyzing 138 recorded interview videos (total duration of 10.5 hours) of 69 internship-seeking students from Massachusetts Institute of Technology (MIT) as they spoke with professional career counselors. Our automated analysis includes facial expressions (e.g., smiles, head gestures), language (e.g., word counts, topic modeling), and prosodic information (e.g., pitch, intonation, pauses) of the interviewees. We derive the ground truth labels by averaging over the ratings of 9 independent judges. Our framework automatically predicts the ratings for interview traits such as excitement, friendliness, and engagement with correlation coefficients of 0.73 or higher, and quantifies the relative importance of prosody, language, and facial expressions. According to our framework, it is recommended to speak more fluently, use less filler words, speak as “we” (vs. “I”), use more unique words, and smile more.
机译：尽管是合格的候选人，但仍想知道为什么你被拒绝了工作？什么地方出了错？在本文中，我们提供了计算框架，以在求职面试的背景下量化人类行为。我们通过分析来自Massachusetts技术研究所（MIT）的69名录制的专业学生（MIT），分析了138名录制的访谈视频（总持续时间）建立了一个模型。我们的自动分析包括面部表情（例如，微笑，头部姿势），语言（例如，字数，主题建模），以及受访者的韵律信息（例如，音调，语调，停顿）。我们通过平均9名独立法官的评级来派生地面真理标签。我们的框架自动预测采访特征的评级，例如兴奋，友好和与0.73或更高的相关系数的接触，并量化韵律，语言和面部表情的相对重要性。根据我们的框架，建议更流利地说出更流利的，使用更少的填充词，用“我们”（与“我”）说，使用更独特的单词，笑得更多。

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