Language Model Adaptation through TOpic Decomposition and MDI Estimation

This work presents a language model adaptation method combining the latent semantic analysis framework with the minimum discrimination information estimation criterion. In particular, an unsupervised topic model decomposition is built which allows to infer topic related word distributions from very short adaptation texts. The resulting word distribution is then used to contraint the estimation of a minimum divergence trigram language. With respect to previous work, implementation detais are discussed that make such approach effective for a large scale application. Experimental results are provided for a digital library indexing task, i.e. the speech transcription of five historical documentary films. By adapting a trigram language model from very terse content descriptions, i.e. maximum ten words, available for each film a word error rate relative reduction of 3.2% was achieved