Using out-of-domain data to improve in-domain language models

Standard statistical language modeling techniques suffer from sparse data problems when applied to real tasks in speech recognition, where large amounts of domain-dependent text are not available. We investigate new approaches to improve sparse application-specific language models by combining domain dependent and out-of-domain data, including a back-off scheme that effectively leads to context-dependent multiple interpolation weights, and a likelihood-based similarity weighting scheme to discriminatively use data to train a task-specific language model. Experiments with both approaches on a spontaneous speech recognition task (switchboard), lead to reduced word error rate over a domain-specific n-gram language model, giving a larger gain than that obtained with previous brute-force data combination approaches.