Data augmentation and language model adaptation using singular value decomposition

Abstract

A new augmentation method for counts to be used in language modeling is presented. It is based on word representations in a reduced space obtained with Singular Value Decomposition. A contribution to a count for a linguistic event x is obtained from the counts of observed events smoothed with a function of their distance from x. Experimental results on a spoken dialogue corpus show the performance of the proposed method, combined with maximum a posteriori probability adaptation, in terms of word error rate reduction.