Consonant Classification Based on Tongue Tip Trajectories

In this thesis, I investigate an issue that is foundational to the development of a new class of novel game-based speech therapies. Whereas several prior computer-based approaches have focused on the use of clinical objectives that concern spatialized aspects of the tongue-tip trajectory (e.g., the targeting of improved accuracy in lingual-palate contact for certain phonemic segments), this line of inquiry focuses on the potential use of attributes relating to the speed and velocity of the tongue-tip trajectory as an alternative clinical objective. I situate my work in the body of prior work on the velocity characteristics of different phonemic segments. For speed and velocity-based clinical targets to be viable, however, it is necessary to characterize and to analyze the relative amounts of variability among and within talkers and phonemic segments with respect to speed-related characteristics. I describe our approach and the results of an analysis which focuses on a large kinematic speech dataset that includes multiple repetitions of 8 different phonemic segments (/t/, /d/, /k/, /g/) as plosives, (/s/, /sh/, /z/) as fricatives and (/tch/) as affricate by 17 talkers. Last, we provide an illustration of how such normative (albeit speaker-dependent) speed and velocity profiles would be instantiated via an interactive scenario that could be included within an extant computer-based speech therapy system. I will represent the classification accuracy results of kinematic data using HMM and SVM classification techniques.