This paper evaluates the performance of various machine learning approaches and their combination for text based dialog act (DA) classification of meetings data. For this task, boosting and three other text based approaches previously described in the literature are used. To further improve the classification performance, three different combination schemes take into account the results of the individual classifiers. All classification methods are evaluated on the ICSI Meeting Corpus based on both reference transcripts and the output of a speech-to-text (STT) system. The results indicate that both the proposed boosting based approach and a method relying on maximum entropy substantially outper-form the use of mini language models and a simple scheme relying on cue phrases. The best performance was achieved by combining individual methods with a multilayer perceptron.
['Matthias Zimmermann', 'Dilek Hakkani-Tür', 'Elizabeth Shriberg', 'Andreas Stolcke']
Text based dialog act classification for multiparty meetings