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Application and Evaluation of a Conditioned Hidden Markov Model for Estimating Interaction Quality of Spoken Dialogue Systems

  • Stefan UltesEmail author
  • Robert ElChab
  • Wolfgang Minker
Conference paper

Abstract

The interaction quality (IQ) metric has recently been introduced for measuring the quality of spoken dialogue systems (SDSs) on the exchange level. While previous work relied on support vector machines (SVMs), we evaluate a conditioned hidden Markov model (CHMM) which accounts for the sequential character of the data and, in contrast to a regular hidden Markov model (HMM), provides class probabilities. While the CHMM achieves an unweighted average recall (UAR) of 0.39, it is outperformed by regular HMM with an UAR of 0.44 and a SVM with an UAR of 0.49, both trained and evaluated under the same conditions.

Keywords

Support Vector Machine Hide Markov Model User Satisfaction State Belief Hide State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.Institute of Communications TechnologyUlmGermany

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