A stochastic model of speech incorporating hierarchical nonstationarity

The concept of two-level (global and local) hierarchical nonstationarity is introduced to describe the elastic and dynamic nature of the speech signal. A doubly stochastic process model is developed to implement this concept. In the model, the global nonstationarity is embodied through an underlying Markov chain that governs evolution of the parameters in a set of output stochastic processes. The local nonstationarity is realized by utilizing state-conditioned, time-varying first- and second-order statistics in the output data-generation process models. For potential uses in automatic uncovering of relationally invariant properties from the speech signal and in speech recognition, the local nonstationarity is represented in a parametric form. Preliminary experiments on fitting the models to speech data demonstrate superior performances of the proposed model to several traditional types of hidden Markov models