Markov Processes on Curves

We study the classification problem that arises when two variables—one continuous (x), one discrete (s)—evolve jointly in time. We suppose that the vector x traces out a smooth multidimensional curve, to each point of which the variable s attaches a discrete label. The trace of s thus partitions the curve into different segments whose boundaries occur where s changes value. We consider how to learn the mapping between the trace of x and the trace of s from examples of segmented curves. Our approach is to model the conditional random process that generates segments of constant s along the curve of x. We suppose that the variable s evolves stochastically as a function of the arc length traversed by x. Since arc length does not depend on the rate at which a curve is traversed, this gives rise to a family of Markov processes whose predictions are invariant to nonlinear warpings (or reparameterizations) of time. We show how to estimate the parameters of these models—known as Markov processes on curves (MPCs)—from labeled and unlabeled data. We then apply these models to two problems in automatic speech recognition, where x are acoustic feature trajectories and s are phonetic alignments.