Semi-automatic language model acquisition without large corpora

Statistical language models have gained a reputation as providing the overall performance for speech recognition, and so widely used in speech recognition systems today. The tasks to which statistical language models can be applied are, however, limited, because a large corpus is essential for the building of a statistical model, and the collection of a new corpus is a very costly task in terms of time and e ort. Thus, if our aim is to apply speech recognition to various tasks as required, we need a way of developing a new language model for a given task at a reasonable cost.