Rao-Blackwellised Gibbs sampling for switching linear dynamical systems

This paper describes the application of Rao-Blackwellised Gibbs sampling (RBGS) to speech recognition using switching linear dynamical systems (SLDS). The SLDS is a hybrid of standard hidden Markov models (HMM) and linear dynamical systems. It is an extension of the stochastic segment model as it relaxes the assumption of independent segments. SLDS explicitly take into account the strong co-articulation present in speech. Unfortunately, inference in SLDS is intractable unless the discrete state sequence is known. RBGS is one approach that may be applied for both improved training and decoding for this form of intractable model. The theory of SLDS and RBGS is described, along with an efficient proposal mechanism. The performance of the SLDS using RBGS for training and inference is evaluated on the ARPA Resource Management task.