Word-level language modeling for P300 spellers based on discriminative graphical models

Word-level language modeling for P300 spellers based on discriminative graphical models.

OBJECTIVE In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers. APPROACH This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilist...

A Probabilistic Graphical Model for Word-Level Language Modeling in P300 Spellers

Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model-based framework and an associated classification algorithm that uses learned statistical prior models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller. The proposed approach models al...

Discriminative Graphical Models for Context-Based Classification

Minimum word error based discriminative training of language models

This paper considers discriminative training of language models for large vocabulary continuous speech recognition. The minimum word error (MWE) criterion was explored to make use of the word confusion information as well as the local lexical constraints inherent in the acoustic training corpus, in conjunction with those constraints obtained from the background text corpus, for properly guiding...

Discriminative Pruning of Language Models for Chinese Word Segmentation

This paper presents a discriminative pruning method of n-gram language model for Chinese word segmentation. To reduce the size of the language model that is used in a Chinese word segmentation system, importance of each bigram is computed in terms of discriminative pruning criterion that is related to the performance loss caused by pruning the bigram. Then we propose a step-by-step growing algo...