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The use of vocabulary-independent (VI) models to improve the usability of speech recognizers is described. Initial results using generalized triphones as VI models show that with more training data and more detailed modeling, the error rate of VI models can be reduced substantially. For example, the error rates for VI models with 5000, 10000, and 15000 training sentences, are 23.9%, 15.2%, and 13.3%, respectively. Moreover, if task-specific training data are available, one can interpolate them with VI models. This task adaptation can reduce the error rate by 18% over task-specifying models.>
Hon et al. (Wed,) studied this question.