Tutorial 3: Unsupervised Speech and Language Processing via Topic Models
In this tutorial, we will present state-of-art machine learning approaches for speech and language processing with highlight on the unsupervised methods for structural learning from the unlabeled sequential patterns. In general, speech and language processing involves extensive knowledge of statistical models. We require designing a flexible, scalable and robust system to meet heterogeneous and nonstationary environments in the era of big data. This tutorial starts from an introduction of unsupervised speech and language processing based on factor analysis and independent component analysis. The unsupervised learning is generalized to a latent variable model which is known as the topic model. The evolution of topic models from latent semantic analysis to hierarchical Dirichlet process, from non-Bayesian parametric models to Bayesian nonparametric models, and from single-layer model to hierarchical tree model shall be surveyed in an organized fashion. The inference approaches based on variational Bayesian and Gibbs sampling are introduced. We will also present several case studies on topic modeling for speech and language applications including language model, document model, retrieval model, segmentation model and summarization model. At last, we will point out new trends of topic models for speech and language processing.