User Adaptive Restoration for Incorrectly-Segmented Utterances in Spoken Dialogue Systems
Kazunori Komatani, Naoki Hotta, Satoshi Sato, Mikio Nakano · 2015
Ideally, the users of spoken dialogue systems should be able to speak at their own tempo.The systems thus need to correctly interpret utterances from various users, even when these utterances contain disfluency.In response to this issue, we propose an approach based on a posteriori restoration for incorrectly segmented utterances.A crucial part of this approach is to classify whether restoration is required or not.We improve the accuracy by adapting the classifier to each user.We focus on the dialogue tempo of each user, which can be obtained during dialogues, and determine the correlation between each user's tempo and the appropriate thresholds for the classification.A linear regression function used to convert the tempos into thresholds is also derived.Experimental results showed that the proposed user adaptation for two classifiers, thresholding and decision tree, improved the classification accuracies by 3.0% and 7.4%, respectively, in ten-fold cross validation.