In dyadic human interactions, mutual influence - a person's influence on the interacting partner's behaviors - is shown to be important and could be incorporated into the modeling framework in characterizing, and automatically recognizing the participants' states. We propose a Dynamic Bayesian Network (DBN) to explicitly model the conditional dependency between two interacting partners' emotion states in a dialog using data from the IEMOCAP corpus of expressive dyadic spoken interactions. Also, we focus on automatically computing the Valence-Activation emotion attributes to obtain a continuous characterization of the participants' emotion flow. Our proposed DBN models the temporal dynamics ofthe emotion states as well as the mutual influence between speakers in a dialog. With speech based features, the proposed network improves classification accuracy by 3.67% absolute and 7.12% relative over the Gaussian Mixture Model (GMM) baseline on isolated turn-by-turn emotion classification.