Phoneme Classification using Constrained Variational Gaussian Process Dynamical System

Part of Advances in Neural Information Processing Systems 25 (NIPS 2012)

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Hyunsin Park, Sungrack Yun, Sanghyuk Park, Jongmin Kim, Chang Yoo


This paper describes a new acoustic model based on variational Gaussian process dynamical system (VGPDS) for phoneme classification. The proposed model overcomes the limitations of the classical HMM in modeling the real speech data, by adopting a nonlinear and nonparametric model. In our model, the GP prior on the dynamics function enables representing the complex dynamic structure of speech, while the GP prior on the emission function successfully models the global dependency over the observations. Additionally, we introduce variance constraint to the original VGPDS for mitigating sparse approximation error of the kernel matrix. The effectiveness of the proposed model is demonstrated with extensive experimental results including parameter estimation, classification performance on the synthetic and benchmark datasets.