Improved language modelling through better language model evaluation measures

摘要

This paper explores the interaction between a language model's perplexity and its effect on the word error rate of a speech recognition system. Much recent research has indicated that these two measures are not as well correlated as was once thought, and many examples exist of models which have a much lower perplexity than the equivalent N-gram model, yet lead to no improvement in recognition accuracy.