Optimal filtering and smoothing for speech recognition using a stochastic target model
Presents a stochastic target model of speech production, where articulator motion in the vocal tract is represented by the state of a Markov-modulated linear dynamical system, driven by a piecewise-deterministic control trajectory and observed through a non-linear function representing the articulatory-acoustic mapping. Optimal filtering and smoothing algorithms for estimating the hidden states of the model from acoustic measurements are derived using a measure-change technique and require the solution of recursive integral equations. A sub-optimal approximation is developed and illustrated using examples taken from real speech.