Abstract
Investigates the perplexity and word error rate performance of two different forms of class model and the respective data-driven algorithms for obtaining automatic word classifications. The computational complexity of the algorithm for the 'conventional' two-sided class model is found to be unsuitable for very large vocabularies (>100k) or large numbers of classes (>2000). A one-sided class model is therefore investigated and the complexity of its algorithm is found to be substantially less in such situations. Perplexity results are reported on both English and Russian data. For the latter both 65k and 430k vocabularies are used. Lattice rescoring experiments are also performed on an English language broadcast news task. These experimental results show that both models, when interpolated with a word model, perform similarly well. Moreover, classifications are obtained for the one-sided model in a fraction of the time required by the two-sided model, especially for very large vocabularies.
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