Large vocabulary speech recognition with multispan statistical language models

Multispan language modeling refers to the integration of various constraints, both local and global, present in the language. It was recently proposed to capture global constraints through the use of latent semantic analysis, while taking local constraints into account via the usual n-gram approach. This has led to several families of data-driven, multispan language models for large vocabulary speech recognition. Because of the inherent complementarity in the two types of constraints, the multispan performance, as measured by perplexity, has been shown to compare favorably with the corresponding n-gram performance. The objective of this work is to characterize the behavior of such multispan modeling in actual recognition. Major implementation issues are addressed, including search integration and context scope selection. Experiments are conducted on a subset of the Wall Street Journal (WSJ) speaker-independent, 20000-word vocabulary, continuous speech task. Results show that, compared to standard n-gram, the multispan framework can lead to a reduction in average word error rate of over 20%. The paper concludes with a discussion of intrinsic multi-span tradeoffs, such as the influence of training data selection on the resulting performance.