Abstract

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 …

Date

2019

Authors

Sandeep Nallan Chakravarthula, Brian Baucom, Shrikanth Narayanan, Panayiotis Georgiou

Journal

arXiv e-prints

Pages

arXiv: 1911.09515