Spoken dialogue systems provide an opportunity for man machine interaction using spoken language as the medium of interaction. In recent years reinforcement learning-based dialogue policy optimization has evolved to be state of the art. In order to cope with the data requirement for policy optimization and also to evaluate dialogue policies user simulators are introduced. Almost all existing data driven methods for user modelling aims at simulating some generic user behavior from some reference dialogue corpus. However, this corpus consists of dialogues from multiple users and thus exhibit different user behaviors. In this paper we explore the possibility of identifying and simulating different user behaviors observed in the corpus. For this purpose inverse reinforcement learning-based user simulation method is employed. Using experimental results, we validate the effectiveness of the proposed method for building multiple behavior specific user simulators.