Dynamic Bayesian networks for automatic speech recognition

Abstract

State-of-the-art automatic speech recognition (ASR) systems are based on probabilistic modelling of the speech signal using Hidden Markov Models. The limitations of these systems under real life conditions arose a question about the robustness of the underlying acoustic modelling methodology. The scope of my thesis is to explore the formalism of Probabilistic Graphical Models, particularly Dynamic Bayesian Networks, from a theoretical and practical point of view, with the aim of developing reliable models of speech and of developing robust ASR systems.