Thesis (MScEng (Electrical and Electronic Engineering))--University of Stellenbosch, 2009. / Statistical speech recognition systems typically utilise a set of statistical models of subword units based on the set of phonemes in a target language. However, in continuous speech it is important to consider co-articulation e ects and the interactions between neighbouring sounds, as over-generalisation of the phonetic models can negatively a ect system accuracy. Traditionally co-articulation in continuous speech is handled by incorporating contextual information into the subword model by means of context-dependent models, which exponentially increase the number of subword models. In contrast, transitional models aim to handle co-articulation by modelling the interphone dynamics found in the transitions between phonemes. This research aimed to perform an objective analysis of diphones as subword units for use in hidden Markov model-based continuous-speech recognition systems, with special emphasis on a direct comparison to a context-dependent biphone-based system in terms of complexity, accuracy and computational e ciency in similar parametric conditions. To simulate practical conditions, the experiments were designed to evaluate these systems in a low resource environment { limited supply of training data, computing power and system memory { while still attempting fast, accurate phoneme recognition. Adaptation techniques designed to exploit characteristics inherent in diphones, as well as techniques used for e ective parameter estimation and state-level tying were used to reduce resource requirements while simultaneously increasing parameter reliability. These techniques include diphthong splitting, utilisation of a basic diphone grammar, diphone set completion, maximum a posteriori estimation and decision-tree based state clustering algorithms. The experiments were designed to evaluate the contribution of each adaptation technique individually and subsequently compare the optimised diphone-based recognition system to a biphone-based recognition system that received similar treatment. Results showed that diphone-based recognition systems perform better than both traditional phoneme-based systems and context-dependent biphone-based systems when evaluated in similar parametric conditions. Therefore, diphones are e ective subword units, which carry suprasegmental knowledge of speech signals and provide an excellent compromise between detailed co-articulation modelling and acceptable system performance