An introduction of Gaussian processes and deep Gaussian processes and their applications to speech processing

抄録

Gaussian process (GP) is a distribution of functions, which can be used for a machine learning framework. GP regression has characteristics of Bayesian model, which can predict uncertainty of outputs, and kernel methods, which enables nonlinear function with a small number of parameters. In this paper, we first describe the basic of GP regression, and introduce recent notable advances of GP. Specifically, we focus on stochastic variational GP that is an approximation method available for a huge amount of training data, and explain a GP-based deep architecture model called deep Gaussian process. Since GP regression is a general-purpose machine learning framework, there are many applications. In this paper, we introduce GP-based applications to speech information processing including speech synthesis.