Whole word discriminative point process models

This paper introduces a discriminative extension to whole-word point process modeling techniques. Meant to circumvent the strong independence assumptions of their generative predecessors, discriminative point process models (DPPM) are trained to distinguish the composite temporal patterns of phonetic events produced for a given word from those of its impostors. Using correct and incorrect word hypotheses extracted from large vocabulary recognizer lattices, we train whole-word DPPMs to provide an alternative set of acoustic model scores. Using solely the timing of sparse phonetic events, DPPM scores exhibit comparable discriminative power to those produced by a state-of-the-art acoustic model built using the IBM Attila Speech Recognition Toolkit. In addition, the inherent complementarity of frame-based and event-based models permits significant improvements from score combination.