Total: 1
This paper describes unsupervised adaptation of language model for many related target domains. In mobile speech input, subject and vocabulary of the language depend highly on the usage context. We use automatically transcribed speech data to select a subset from the language model training data for building a maximum entropy model adapted to speech input. This model is further adapted for most popular mobile applications. When used in interpolation with the background N-gram model, the adapted models give over 10% relative word error rate reduction in Estonian mobile speech input experiments.