Speech recognition performance is severely affected when the lexical, syntactic, or semantic
characteristics of the discourse in the training and recognition tasks differ. The aim of …

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

Markov models are extremely useful as a general, widely applicable tool for many areas in
statistical pattern recognition. This unique text/reference places the formalism of Markov …

This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in
continuous speech recognition. Most of today's state-of-the-art speech recognition systems …

A survey of research on spoken language understanding is presented. It covers aspects of
knowledge representation, automatic interpretation strategies, semantic grammars …

Recurrent neural network language models (RNNLM) have become an increasingly popular
choice for state-of-the-art speech recognition systems due to their inherently strong …

This paper reviews methods for language model adaptation. Paradigms and basic methods
are first introduced. Basic theory is presented for maximum a-posteriori estimation, mixture …

A new method is presented to quickly adapt a given language model to local text
characteristics. The basic approach is to choose the adaptive models as close as possible to …

A method, apparatus, and medium for generating a grammar network for speech recognition
and a dialogue speech recognition are provided. A method, apparatus, and medium for …

Statistical learning and probabilistic prediction are fundamental processes in auditory
cognition. A prominent computational model of these processes is Prediction by Partial …