No standardised assessment exists for the recognition and quantification of acquired speech dyspraxia (also called apraxia of speech, AS). This thesis aims to work towards development of such an assessment based on perceptual features. Review of previous features claimed to characterise AS and differentiate it from other acquired pronunciation problems (dysarthrias; phonemic paraphasia - PP) has proved negative. Reasons for this have been explored. A reconceptualisation of AS is attempted based on physical studies of AS, PP and the dysarthrias; their position and relationship within coalitional models of speech production; by comparison with normal action control and other dyspraxias. Contrary to the view of many it is concluded that AS and PP are dyspraxias (albeit different types). However, due to the interactive nature of speech-language production and behaviour of the vocal tract as a functional whole AS is unlikely to be distinguishable in an absolute fashion based on single speech characteristics. Rather it is predicted that pronunciation disordered groups will differ relatively on total error profiles and susceptibility to associated effects (variability; propositionality; struggle; length-complexity; latency-utterance times). Using a prototype battery and refined error transcription and analysis procedures a series of studies test predictions on three groups: spastic dysarthrics (n = 6) AS and PP without (n = 12) and with (n = 12) dysphasia. The main conclusions do not support the error profile hypotheses in any straightforward manner. Length-complexity effects and latency-utterance times fail to consistently separate groups. Variability, propositionality and struggle proved the most reliable indicators. Error profiles remain the closest indicators of speakers' intelligibility and therapeutic goals. The thesis argues for a single case approach to differential diagnosis and alternative statistical analyses to capture individual and group differences. Suggestions for changes to the prototype clinical battery and data management to effect optimal speaker differentiation conclude the work
