A hierarchical point process model for speech recognition

In this paper, we present a computational framework to engage distinctive feature-based theories of speech perception. Our approach involves: (i) transforming the signal into a collection of marked point processes, each consisting of distinctive feature landmarks determined by statistical learning methods, and (ii) using the temporal statistics of this sparse representation to probabilistically decode the underlying phonological sequence. In order to assess the viability of this approach, we benchmark our performance on broad class recognition against a range of HMM-based approaches using the CMU Sphinx 3 system. We find our system to be competitive with this baseline and conclude by outlining various avenues for future development of our methodology.