The lack of freely available state-of-the-art Speech-to-Text (STT) software has been a major hindrance to the development of new audio information processing technology. The high cost of the infrastructure required to conduct state-of-the-art speech recognition research prevents many small research groups from evaluating new ideas on large-scale tasks. In this paper, we present the core components of an available state-of-the-art STT system: an acoustic processor which converts the speech signal into a sequence of feature vectors; a training module which estimates the parameters for a Hidden Markov Model; a linguistic processor which predicts the next word given a sequence of previously recognized words; and a search engine which finds the most probable word sequence given a set of feature vectors. 1.