Hierarchical modeling is a fundamental concept in Bayesian statistics. The basic idea is that
parameters are endowed with distributions which may themselves introduce new …

Abstract Automatic Speech Recognition (ASR) systems can be trained to achieve
remarkable performance given large amounts of manually transcribed speech, but large …

It is with renewed appreciation for the size of the task that I come to writing the present
chapter. The number of special issues of journals dedicated to im/politeness these days …

What are the units of text that we want to model? From bytes to multi-word expressions, text
can be analyzed and generated at many granularities. Until recently, most natural language …

We present the nested Chinese restaurant process (nCRP), a stochastic process that
assigns probability distributions to ensembles of infinitely deep, infinitely branching trees …

Topic models are statistical models for learning the latent structure in document collections,
and have gained much attention in the machine learning community over the last decade …

Since the experiments of Saffran et al.[Saffran, J., Aslin, R., & Newport, E.(1996). Statistical
learning in 8-month-old infants. Science, 274, 1926–1928], there has been a great deal of …

We present a novel approach to speech processing based on the principle of pattern
discovery. Our work represents a departure from traditional models of speech recognition …

In this paper, we propose a new Bayesian model for fully unsupervised word segmentation
and an efficient blocked Gibbs sampler combined with dynamic programming for inference …

This paper introduces adaptor grammars, a class of probabilistic models of language that
generalize probabilistic context-free grammars (PCFGs). Adaptor grammars augment the …