Speech recognition experiments using multi-span statistical language models

A multi-span framework was proposed to integrate the various constraints, both local and global, that are present in the language. In this approach, local constraints are captured via n-gram language modeling, while global constraints are taken into account through the use of latent semantic analysis. The performance of the resulting multi-span language models, as measured by the perplexity, has been shown to compare favorably with the corresponding n-gram performance. This paper reports on actual speech recognition experiments, and shows that word error rate is also substantially reduced. On a subset of the Wall Street Journal speaker-independent, 20,000-word vocabulary, continuous speech task, the multi-span framework resulted in a reduction in average word error rate of up to 17%.