using bi-modal alignment and clustering techniques for documents and speech thematic segmentations
In this paper, we describe a new method for a simultaneous thematic segmentation of the meeting dialogs and the documents discussed or visible throughout the meeting. This bi-modal method is suitable for multimodal applications that are centered on documents, such as meetings and lectures, where documents can be aligned with meeting dialogs. Bringing into play this alignment, our bi-modal segmentation method first transforms its results into a set of nodes in a 2D graph space, where the two axes represent respectively the document units and the meeting dialogs units. Secondly, via a clustering method, the most connected regions in the constituted bi-graph are detected. Finally, the denser clusters are projected on the two axes. The two sequences of segments, obtained on both axes, represent the thematic structure of the document and of the meeting dialogs respectively. We present in this article this bi-modal segmentation technique and its performance compared with two mono-modal segmentation methods.
thematic segmentation k-means clustering thematic alignment