The aim of this work is to apply a sampling approach to speech modeling, and propose a Gibbs sampling based Multi-scale Mixture Model (M3). The proposed approach focuses on the multi-scale property of speech dynamics, i.e., dynamics in speech can be observed on, for instance, short-time acoustical, linguistic-segmental, and utterance-wise temporal scales. M 3 is an extension of the Gaussian mixture model and is considered a hierarchical mixture model, where mixture components in each time scale will change at intervals of the corresponding time unit. We derive a fully Bayesian treatment of the multi-scale mixture model based on Gibbs sampling. The advantage of the proposed model is that each speaker cluster can be precisely modeled based on the Gaussian mixture model unlike conventional single-Gaussian based speaker clustering (e.g., using the Bayesian Information Criterion (BIC)). In addition, Gibbs sampling offers the potential to avoid a serious local optimum problem. Speaker clustering experiments confirmed these advantages and obtained a significant improvement over the conventional BIC based approaches.