Classification of Correction Turns in Multilingual Dialogue Corpus

This paper presents a multiclass classification of correction dialog turns using machine learning. The classes are determined by the type of the introduced recognition errors while performing WOz trials and creating the multilingual corpus. Three datasets were obtained using different sets of acoustic-prosodic features on the multilingual dialogue corpus. The classification experiments were done using different machine learning paradigms: Decision Trees, Support Vector Machines and Deep Learning. After careful experiments setup and optimization on the hyper-parameter space, the obtained classification results were analyzed and compared in the terms of accuracy, precision, recall and F1 score. The achieved results are comparable with those obtained in similar experiments on different tasks and speech databases.