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IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of mathematics (3rd ed.)
Handbook of mathematics (3rd ed.)
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Simultaneous translation of lectures and speeches
Machine Translation
Performance prediction for exponential language models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Topic-Dependent Language Model with Voting on Noun History
ACM Transactions on Asian Language Information Processing (TALIP)
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UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
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Many groups have investigated the relationship of word error rate and perplexity of language models. This issue is of central interest because perplexity optimization can be done independent of a recognizer and in most cases it is possible to find simple perplexity optimization procedures. Moreover, many tasks in language model training such as the optimization of word classes may use perplexity as target function resulting in explicit optimization formulas which are not available if error rates are used as target. This paper first presents some theoretical arguments for a close relationship between perplexity and word error rate. Thereafter the notion of uncertainty of a measurement is introduced and is then used to test the hypothesis that word error rate and perplexity are correlated by a power law. There is no evidence to reject this hypothesis.