A shallow model of backchannel continuers in spoken dialogue
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
University of Colorado dialog systems for travel and navigation
HLT '01 Proceedings of the first international conference on Human language technology research
SMOTE: synthetic minority over-sampling technique
Journal of Artificial Intelligence Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
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Verbal feedback is an important information source in establishing interactional rapport. However, predicting verbal feedback across languages is challenging due to language-specific differences, inter-speaker variation, and the relative sparseness and optionality of verbal feedback. In this paper, we employ an approach combining classifier weighting and SMOTE algorithm oversampling to improve verbal feedback prediction in Arabic, English, and Spanish dyadic conversations. This approach improves the prediction of verbal feedback, up to 6-fold, while maintaining a high overall accuracy. Analyzing highly weighted features highlights widespread use of pitch, with more varied use of intensity and duration.