The relevance of timing, pauses and overlaps in dialogues: Detecting topic changes in scenario based meetings

We present an investigation of the relevance of simple conversational features as indicators of topic shifts in small-group meetings. Three proposals for representation of dialogue data are described, and their effectiveness assessed at detecting topic boundaries on a large section of the Augmented Multi-Party Interaction (AMI) corpus. These proposals consist in representing a speech event through combinations of features such as the lengths of vocalisations, pauses and speech overlaps in the immediate temporal context of the event. Results show that timing of vocalisations alone, within a 7-vocalisation window (3 on each side of the vocalisation under consideration), can be an effective predictor of topic boundaries, outperforming topic segmentation methods based on lexical features. Pause and overlap information on their own also yield comparably good segmentation accuracy, suggesting that simple methods could complement or even serve as alternatives to methods which require more demanding speech processing for meeting browsing.