We investigate the problem of acoustic modeling in which prior language-specific knowledge and transcribed data are unavailable. We present an unsupervised model that simultaneously segments the speech, discovers a proper set of sub-word units (., phones) and learns a Hidden Markov Model (HMM) for each induced acoustic unit.