Advanced Probabilistic Models for Speech and Language

This project proposal applies to the statistical and machine learning models used in speech and language research. It aims to develop a more general theoretical framework for the use of state-space models in speech and language, and to expand the set of models currently used in these fields. Probabilistic Models State-Space Models Generalized SSMs Nonlinear SSMs Bayesian Learning Inferring Trajectories Learning Example Conclusion Title Page J I Page 2 of 10 Go Back Full Screen Quit 1. Probabilistic Models Modeling speech and language demands the development and use of appropriate probabilistic models. These models are necessary for: 1. Machine Learning: Probabilistic models provide a general means by which to derive and analyse algorithms. 2. Neural-Network Modeling: Probabilistic models provide statistical interpretations of neural network models of language processing. 3. Data-analysis: Probabilistic models facilitate data-analysis in the experimental analysis of human language abilities. Suitable models should describe, for example, the sequential and recursive structures found in the these domains. Probabilistic Models State-Space Models Generalized SSMs Nonlinear SSMs Bayesian Learning Inferring Trajectories Learning Example Conclusion Title Page J I Page 3 of 10 Go Back Full Screen Quit 2. State-Space Models In state-space models, the observed data are a function of a state-space that is evolving through time. Observed−Variables Latent Variables PSfrag replacements