Conversations are at the core of everyday social interactions. The interactions between conversants are preformed within the realm of a sophisticated and self-managed turn taking system. In human conversations, the turn taking system supports minimal speaker overlap during turn transitions and minimum gaps between turns. Spoken dialogue systems are a new form of conversational user interface that permits users to use their voice to interact with the computer. As such, the turn taking capabilities of SDS should evolve from a simple timeout to a more human-like model. Recent advances in turn taking systems for SDS use different local features of the last few utterances to predict turn transition. This thesis explores using a summary of past speaker behavior to better predict turn transitions. We believe that the summary features represent an evolving model of the other conversant.