Call classification for automated troubleshooting on large corpora

  title={Call classification for automated troubleshooting on large corpora},
  author={Keelan Evanini and David Suendermann-Oeft and Roberto Pieraccini},
  journal={2007 IEEE Workshop on Automatic Speech Recognition & Understanding (ASRU)},
This paper compares six algorithms for call classification in the framework of a dialog system for automated troubleshooting. The comparison is carried out on large datasets, each consisting of over 100,000 utterances from two domains: television (TV) and Internet (INT). In spite of the high number of classes (79 for TV and 58 for INT), the best classifier (maximum entropy on word bigrams) achieved more than 77% classification accuracy on the TV dataset and 81% on the INT dataset. 

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