This paper describes the Graphical Models Toolkit (GMTK), an open source, publically available toolkit for developing graphical-model based speech recognition and general time series systems. Graphical models are a flexible, concise, and expressive probabilistic modeling framework with which one may rapidly specify a vast collection of statistical models. This paper begins with a brief description of the representational and computational aspects of the framework. Following that is a detailed

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... scription of GMTK's features, including a language for specifying structures and probability distributions, logarithmic space exact training and decoding procedures, the concept of switching parents, and a generalized EM training method which allows arbitrary sub-Gaussian parameter tying. Taken together, these features endow GMTK with a degree of expressiveness and functionality that significantly complements other publically available packages. GMTK was recently used in the 2001 Johns Hopkins Summer Workshop, and experimental results are described in detail both herein and in a companion paper.