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. 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.

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