A new approach is proposed for the clustering of words in a given vocabulary. The method is based on a paradigm first, formulated in the context, of information retrieval, called latent semuntac unulysis. This paradigm leads to a parsimonious vector representation of each word in a suitable vector space, where familiar clustering techniques can be applied. The distance measure selected in this space arises naturally from the problem formulation. Preliminary experiments indicate that the clusters produced are intuitively satisfactory. Because these clusters are semantic in nature, this approach may prove useful as a complement, to conventional class-based statistical language modeling techniques.
We describe three analyses on the effects of spontaneous speech on continuous speech recognition performance. We have found that: (1) spontaneous speech effects significantly degrade recognition performance, (2) fluent spontaneous speech yields word accuracies equivalent to read speech, and (3) using spontaneous speech training data can significantly improve performance for recognizing spontaneous speech. We conclude that word accuracy can be improved by explicitly modeling spontaneous effects in the recognizer, and by using as much spontaneous speech training data as possible. Inclusion of read speech training data, even within the task domain, does not significantly improve performance.
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