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Acoustic modelling based on Hidden Markov Models (HMMs) is employed by state-of-the-art stochastic speech recognition systems. Although HMMs are a natural choice to warp the time axis and model the temporal phenomena in the speech signal, they do not model the spectral phenomena well. This is a consequence of their conditionally independent properties, which are inadequate for sequential processing. In this work, a new acoustic modelling paradigm based on Conditional Random Fields (CRFs) is investigated and developed. This paradigm addresses some of the weak aspects of HMMs while maintaining many of the good aspects, which have made them successful. In particular, the acoustic modelling problem is reformulated in a data driven, sparse, augmented space to increase discrimination. In addition, acoustic context modelling is explicitly integrated to handle the sequential phenomena of the speech signal. In a phone recognition task, test results have shown significant improvements over comparable HMM systems. While the theoretical motivations behind this work are attractive, a practical implementation of these complex systems is demanding. The main goal of this work is to present an efficient framework for estimating these models that ensures scalability and generality for large vocabulary speech recognition systems