If this approach could be applied to gesturespeech alignment, it would be advantageous because the binding probabilities could be combined with the output of probabilistic recognizers to produce a pipeline architecture, similar to that proposed in (Wu et al., 1999).
we define a binding, b ∈ B, as a tuple relating a gesture, g ∈ G, to a corresponding speech reference, r ∈ R. Provided G and R, the set B enumerates all possible bindings between them.