TOWARDS IMPROVED LANGUAGE MODEL EVALUATION MEASURES
P.R. Clarkson and A.J. Robinson
Much recent research has demonstrated that the correlation between a language model's perplexity and its effect on the word error rate of a speech recognition system is not as strong as was once thought. This represents a major problem for those involved in developing language models. This paper describes the development of new measures of language model quality. These measures retain the ease of computation and task independence that are perplexity's strengths, yet are considerably better correlated with word error rate. This paper also shows that mixture-based language models are improved by applying interpolation weights which are optimised with respect to these new measures, rather than a maximum likelihood criterion.
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