Phoneme Classification using Constrained Variational Gaussian Process Dynamical System
نویسندگان
چکیده
For phoneme classification, this paper describes an acoustic model based on the variational Gaussian process dynamical system (VGPDS). The nonlinear and nonparametric 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.
منابع مشابه
Phoneme Classification Using Temporal Tracking of Speech Clusters in Spectro-temporal Domain
This article presents a new feature extraction technique based on the temporal tracking of clusters in spectro-temporal features space. In the proposed method, auditory cortical outputs were clustered. The attributes of speech clusters were extracted as secondary features. However, the shape and position of speech clusters change during the time. The clusters temporally tracked and temporal tra...
متن کاملGaussian Process Latent Variable Models for Dimensionality Reduction and Time Series Modeling
Time series data of high dimensions are frequently encountered in fields like robotics, computer vision, economics and motion capture. In this survey paper we look first at Gaussian Process Latent Variable Model (GPLVM) which is a probabilistic nonlinear dimensionality reduction method. Further we discuss Gaussian Process Dynamical Model (GPDMs) which are based GPLVM. GPDM is a probabilistic ap...
متن کاملPROJECTED DYNAMICAL SYSTEMS AND OPTIMIZATION PROBLEMS
We establish a relationship between general constrained pseudoconvex optimization problems and globally projected dynamical systems. A corresponding novel neural network model, which is globally convergent and stable in the sense of Lyapunov, is proposed. Both theoretical and numerical approaches are considered. Numerical simulations for three constrained nonlinear optimization problems a...
متن کاملThe Variational Coupled Gaussian Process Dynamical Model
We present a full variational treatment of the Coupled Gaussian Process Dynamical Model (CGPDM), which is a non-parametric, modular dynamical movement primitive model. Our work builds on similar developments in Gaussian state-space models, but we obviate the need for sampling, which results in a fast deterministic approximation for the posterior of latent states. We illustrate the performance o...
متن کاملVariational Gaussian Process State-Space Models
State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer th...
متن کامل