A multispan language modeling framework for large vocabulary speech recognition

A new framework is proposed to construct multispan language models for large vocabulary speech recognition, by exploiting both local and global constraints present in the language. While statistical n-gram modeling can readily take local constraints into account, global constraints have been more difficult to handle within a data-driven formalism. In this work, they are captured via a paradigm first formulated in the context of information retrieval, called latent semantic analysis (LSA). This paradigm seeks to automatically uncover the salient semantic relationships between words and documents in a given corpus. Such discovery relies on a parsimonious vector representation of each word and each document in a suitable, common vector space. Since in this space familiar clustering techniques can be applied, it becomes possible to derive several families of large-span language models, with various smoothing properties. Because of their semantic nature, the new language models are well suited to complement conventional, more syntactically oriented n-grams, and the combination of the two paradigms naturally yields the benefit of a multispan context. An integrative formulation is proposed for this purpose, in which the latent semantic information is used to adjust the standard n-gram probability. The performance of the resulting multispan language models, as measured by perplexity, compares favorably with the corresponding n-gram performance