Providing sublexical constraints for word spotting within the ANGIE framework

We describe our recent work in implementing a word-spotting system based on the ANGIE framework and the effects of varying the nature of the sublexical constraints placed upon the wordspotter’s filler model. ANGIE is a framework for modelling speech where the morphological and phonological substructures of words are jointly characterized by a context-free grammar and are represented in a multi-layered hierarchical structure. In this representation, the upper layers capture syllabification, morphology, and stress, the preterminal layer represents phonemics, and the bottom terminal categories are the phones. ANGIE provides a flexible framework where we can explore the effects of sublexical constraints within a word-spotting environment. Our experiments with spotting city names in ATIS validate the intuition that increasing the constraints present in the model improves performance, from 85.3 FOM for phone bigram to 89.3 FOM for a word lexicon. They also empirically strengthens our belief that ANGIE provides a feasible framework for various speech recognition tasks, of which word-spotting is one.