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Research trends on SDS evaluation are recently focusing on objective assessment methods. Most existing methods, which derive quality for each systemuser-exchange, do not consider temporal dependencies on the quality of previous exchanges. In this work, we investigate an approach for determining Interaction Quality for human-machine dialogue based on methods modeling the sequential characteristics using HMM modeling. Our approach significantly outperforms conventional approaches by up to 4.5% relative improvement based on Unweighted Average Recall metrics.

Interaction Quality Estimation in Spoken Dialogue Systems Using Hybrid-HMMs