The string regeneration problem is the problem of generating a fluent sentence from a bag of words. We explore the Ngram language model approach to string regeneration. The approach computes the highest probability permutation of the input bag of words under an N-gram language model. We describe a graph-based approach for finding the optimal permutation. The evaluation of the approach on a numb...

In this paper, we present the results that our n-gram based word string language model, combined with speaker and noise adaptation of the acoustic model, improves recognition performance of noisy broadcast news speech. The focus was brought into a remedy against recognition errors of short words. The word string language models based on POS and n-gram frequency reduced deletion errors by 17%, i...

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 generated for a given input semantics. Three models (and several variants) are trained and tested: an n-gram language model, a discriminative maximum entropy model using structural information (and incorporating the lang...

This paper describes a number of techniques for language verification based on acoustic processing and n-gram language modelling. A new technique is described which uses anti-models to model the general class of languages. These models are then used to normalise the acoustic score giving a 34% reduction in the error rate of the system. An approach to automatically generate discriminative subwor...

The similarity language model is a statistical model that makes efficient use of long distance information when possible and falls back to standard ngram language model when not. To estimate the probability distribution of a given target context, each training example of the ngram model is retrieved and its similarity to the target context is estimated. In this work, this is done by performing ...