Spoken Dialog Systems allow users to interact with a Dialog Manager (DM) using natural language, thereby following a goal to fulfill their task. State-of-the-art solutions cast the problem as Markov Decision Process, leveraging Reinforcement Learning (RL) algorithms to find an optimal dialog strategy for the DM. For this purpose, several thousand dialogs need to be seen by the RL agent. A user simulator comes in handy to generate responses on demand, however the current state-of-the-art agenda-based user simulators lack the ability to model real human subjects. In this thesis, this problem is addressed by implementing a user simulator using a Recurrent Neural Network which approximates the agenda-based model in a first step. Going onwards, it is shown to learn noise and variance treated as varying user behavior. This is used to train the simulator on real data thus modeling real users.