We attemped to improve recognition accuracy by reducing the inadequacies of the lexicon and language model. Specifically we address the following three problems: (1) the best size...
Richard M. Schwartz, Long Nguyen, Francis Kubala, ...
In this paper, we propose a new stochastic language model that integrates local and global constraints effectively and describe a speechrecognition system basedon it. Theproposedl...
Many of the kinds of language model used in speech understanding suffer from imperfect modeling of intra-sentential contextual influences. I argue that this problem can be address...
To understand a speaker’s turn of a conversation, one needs to segment it into intonational phrases, clean up any speech repairs that might have occurred, and identify discourse...
We describe a parsing system based upon a language model for English that is, in turn, based upon assigning probabilities to possible parses for a sentence. This model is used in ...
We present a new statistical language model based on a Colnbination of individual word language models. Each word model is built from an individual corpus which is formed by extra...
Statistical language models estimate the probability of a word occurring in a given context. The most common language models rely on a discrete enumeration of predictive contexts ...
John Blitzer, Kilian Q. Weinberger, Lawrence K. Sa...
Two major stages stages in language identification systems can be identified: the language modeling stage, where the distinctive features of languages are determined and stored in...
In this paper we describe and evaluate several statistical models for the task of realization ranking, i.e. the problem of discriminating between competing surface realizations ge...
Genomic IR, characterized by its highly specific information need, severe synonym and polysemy problem, long term name and rapid growing literature size, is challenging IR communit...