Phoneme Classification using Constrained Variational Gaussian Process Dynamical System

For phoneme classification, this paper describes an acoustic model based on the variational Gaussian process dynamical system (VGPDS). The nonlinear and non-parametric acoustic model is adopted to overcome the limitations of classical hidden Markov models (HMMs) in modeling speech. The Gaussian process prior on the dynamics and emission functions respectively enable the complex dynamic structure and long-range dependency of speech to be better represented than that by an HMM. In addition, a variance constraint in the VGPDS is introduced to eliminate the sparse approximation error in the kernel matrix. The effectiveness of the proposed model is demonstrated with three experimental results, including parameter estimation and classification performance, on the synthetic and benchmark datasets.