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An analysis of observation length requirements for machine understanding of human behaviors from spoken language

机译：从口语中对人类行为的观察长度要求分析

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摘要

The task of quantifying human behavior by observing interaction cues is an important and useful one across a range of domains in psychological research and practice. Machine learning-based approaches typically perform this task by first estimating behavior based on cues within an observation window, such as a fixed number of words, and then aggregating the behavior over all the windows in that interaction. The length of this window directly impacts the accuracy of estimation by controlling the amount of information being used. The exact link between window length and accuracy, however, has not been well studied, especially in spoken language. In this paper, we investigate this link and present an analysis framework that determines appropriate window lengths for the task of behavior estimation. Our proposed framework utilizes a two-pronged evaluation approach: (a) extrinsic similarity between machine predictions and human expert annotations, and (b) intrinsic consistency between intra-machine and intra-human behavior relations. We apply our analysis to real-life conversations that are annotated for a large and diverse set of behavior codes and examine the relation between the nature of a behavior and how long it should be observed. We find that behaviors describing negative and positive affect can be accurately estimated from short to medium-length expressions whereas behaviors related to problem-solving and dys-phoria require much longer observations and are difficult to quantify from language alone. These findings are found to be generally consistent across different behavior modeling approaches.
机译：通过观察互动提示量化人类行为的任务是在心理研究和实践中的一系列域中的重要性和有用的一个。基于机器的学习方法通​​常通过基于观察窗口内的提示的第一估计行为来执行此任务，例如固定数量的单词，然后在该交互中聚合在所有窗口中的行为。通过控制所使用的信息量，此窗口的长度直接影响估计的准确性。然而，窗口长度和准确性之间的确切链接尚未得到很好的研究，尤其是口语。在本文中，我们调查此链接并呈现一个分析框架，用于确定行为估计任务的适当窗口长度。我们所提出的框架利用双管齐下的评估方法：（a）机器预测和人类专家注释之间的外在相似性，（b）内部和人类行为关系之间的内在一致性。我们将分析应用于以大型和多样化的行为代码注释的现实生活对话，并检查行为的性质与应该观察到的关系。我们发现描述负面和正面影响的行为可以从短路到中长目的表达准确估计，而与问题解决和心脏病的行为需要更长的观察，并且难以单独量化语言。这些发现通常在不同的行为建模方法中一致。

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  • 来源
    《Computer speech and language》 |2021年第3期|101162.1-101162.24|共24页
  • 作者

    Sandeep Nallan Chakravarthula; Brian R.W. Baucom; Shrikanth Narayanan; Panayiotis Georgiou;

  • 作者单位

    Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA;

    Department of Psychology College of Social & Behavioral Science University of Utah Salt Lake City UT USA;

    Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA;

    Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Human behavior; Spoken language; Observation window length; Machine learning;

    机译：人类行为;口语;观察窗长;机器学习;

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