Development of articulatory-based multilevel segmental HMMs for phonetic classification in ASR

A simple multiple-level HMM is presented in which speech dynamics are modelled as linear trajectories in an intermediate, formant-based representation and the mapping between the intermediate and acoustic data is achieved using one or more linear transformations. An upper-bound on the performance of such a system is established. Experimental results on the TIMIT corpus demonstrate that, if the dimension of the intermediate space is sufficiently high or the number of articulatory-to-acoustic mappings is sufficiently large, then this upper-bound can be achieved.