Data augmentation and language model adaptation

A method is presented for augmenting word n-gram counts in a matrix which represents a 2-gram Language Model (LM). This method is based on numerical distances in a reduced space obtained by Singular Value Decomposition (SVD). Rescoring word lattices in a spoken dialogue application using an LM containing augmented counts has lead to a Word Error Rate (WER) reduction of 6.5%. By further interpolating augmented counts with the counts extracted from a very large newspaper corpus, but only for selected histories, a total WER reduction of 11.7% was obtained. We show that this approach gives better results than a global count interpolation for all histories of the LM.