On Compressing N-Gram Language Models

In large-vocabulary speech recognition systems, the major part of memory resources is typically consumed by a large n-gram language model. Representing the language model compactly is important in recognition systems targeted for small devices with limited memory resources. This paper extends the compressed language model structure proposed earlier by Whittaker and Raj. By separating n-grams that are prefixes to longer n-grams, redundant information can be omitted. Experiments on English 4-gram models and Finnish 6-gram models show that extended structure can achieve up to 30% lossless memory reductions when compared to baseline structure of Whittaker and Raj.