Unsupervised topic modeling for leader detection in spoken discourse

In this paper, we describe a method for leader detection in multi-party spoken discourse that relies on unsupervised topic modeling to segment the discourse automatically. Latent Dirichlet allocation is applied to sliding temporal windows of utterances, resulting in a topic model which captures the fluid transitions from topic to topic which occur in multi-party discourse. Further processing discretizes the continuous topic mixtures into sequential topic segments. Features are extracted from topic shift regions and used to train a binary role classifier. The added topic shift features significantly improve the baseline performance on two corpora, demonstrating both the value of the features and the robustness of the unsupervised segmentation. Furthermore, our classification results on the ICSI corpus, using automatically segmented topics, are better than the results using ground truth segmentations.