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

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 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