Supervised and unsupervised feature selection for inferring social nature of telephone conversations from their content

Anthony Stark, Izhak Shafran, Jeffrey Kaye

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The ability to reliably infer the nature of telephone conversations opens up a variety of applications, ranging from designing context-sensitive user interfaces on smartphones, to providing new tools for social psychologists and social scientists to study and understand social life of different subpopulations within different contexts. Using a unique corpus of everyday telephone conversations collected from eight residences over the duration of a year, we investigate the utility of popular features, extracted solely from the content, in classifying business-oriented calls from others. Through feature selection experiments, we find that the discrimination can be performed robustly for a majority of the calls using a small set of features. Remarkably, features learned from unsupervised methods, specifically latent Dirichlet allocation, perform almost as well as with as those from supervised methods. The unsupervised clusters learned in this task shows promise of finer grain inference of social nature of telephone conversations.

Original languageEnglish (US)
Title of host publication2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings
Pages449-454
Number of pages6
DOIs
StatePublished - Dec 1 2011
Event2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011 - Waikoloa, HI, United States
Duration: Dec 11 2011Dec 15 2011

Publication series

Name2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings

Other

Other2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011
CountryUnited States
CityWaikoloa, HI
Period12/11/1112/15/11

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Supervised and unsupervised feature selection for inferring social nature of telephone conversations from their content'. Together they form a unique fingerprint.

  • Cite this

    Stark, A., Shafran, I., & Kaye, J. (2011). Supervised and unsupervised feature selection for inferring social nature of telephone conversations from their content. In 2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings (pp. 449-454). [6163973] (2011 IEEE Workshop on Automatic Speech Recognition and Understanding, ASRU 2011, Proceedings). https://doi.org/10.1109/ASRU.2011.6163973