On the use of Negative Samples in the MGGI Methodology and its application for Difficult Vocabulary Recognition Tasks

The inference methods which are proposed in Syntactic Pattern Recognition in practice only make use of positive data and generate a heuristic generalization of strings in the data. However, the use of positive data becomes insufficient when very discriminatory models are needed. This is the case of Difficult Vocabularies in Isolated Word Recognition tasks. This paper is a first attempt at using positive and negative data that presents two main characteristics: it respects the computational efficiency with moderate-sized training sets, and it is suitable for tasks in Syntactic Pattern Recognition, specifically in Automatic Speech Recognition.