Use of non-negative matrix factorization for language model adaptation in a lecture transcription task

Abstract

This paper introduces the Non-negative Matrix Factorization for Language Model adaptation. This approach is an alternative to Latent Semantic Analysis based Language Modeling using Singular Value Decomposition (SVD) with several benefits. A new method, which does not require an explicit document segmentation of the training corpus is presented as well. This method resulted in a perplexity reduction of 16% on a database of biology lecture transcriptions.