We propose a novel recognition method for generating an accurate grammatical word-graph allowing grammatical deviations. Our method uses both an n-gram and a grammar-based statistical language model and aligns utterances with the grammar by adding deviation information during the search process. Our experiments confirm that the word-graph obtained by our proposed method is superior to the one o...

We study the new boundary condition of the O(n) model proposed by Jacobsen and Saleur using the matrix model. The spectrum of boundary operators and their conformal weights are obtained by solving the loop equations. Using the diagrammatic expansion of the matrix model as well as the loop equations, we make an explicit correspondence between the new boundary condition of the O(n) model and the ...

An adaptive language modeling method is proposed in this paper. Instead of using one static model for all situations, it applies a set of specific models to dynamically adapt to the discourse. We present the general structure of the model and the training procedure. In our experiments, we instantiated the method with a set of domain dependent models which are trained according to different soci...

Most language models treat speech as simply sequences of words, ignoring the fact that words are also events in time. This paper reports an initial exploration of how word probabilities vary with time-into-utterance, and proposes a method for using this information to improve a language model. This is done by computing the ratio of the probability of the word at a specific time to its overall u...

The length of the word sequence is not taken into account under language modeling of n-gram local probability modeling. Due to this property, the optimal values of the language weight and word insertion penalty for balancing acoustic and linguistic probabilities is affected by the length of word sequence. To deal with this problem, a new language model is developed based on Bernoulli trial mode...

Previous language modeling approaches to information retrieval have focused primarily on single terms. The use of bigram models has been studied, but the restriction on word order and adjacency may not be justified for information retrieval. We propose a new language modeling approach to information retrieval that incorporates lexical affinities, or pairs of words that occur near each other, wi...

We present a new method of introducing domain knowledge into an n-gram language model. It is based on a combination of language models for individual word domains. Each word model is built from an individual corpus which is formed by extracting those subsets of the entire training corpus which contain that significant word. When testing, significant words are extracted from a cache and their mo...