Modeling Liaison using Gradient Symbolic Representations

(Joint work with Paul Smolensky and Eric Rosen, Johns Hopkins University & Microsoft Research)

The Gradient Symbolic Computation framework claims that the mental representations underlying speech are abstract, symbolic, and continuous, such that different symbolic constituents can present within a structure to varying degrees. I’ll discuss how this framework can be used to model the distribution of liaison consonants in French, proposing an algorithm that learns the relative activation of symbolic constituents.