2010 · 39 citations · 12 references
ComputingComputer ScienceComputational LinguisticsMachine LearningNatural Language ProcessingLanguage ModelingLanguage ModelsSpeech RecognitionAccuracy-pruning Trade-offEntropy MinimizationLanguage Model PretrainingLanguage Model SmoothingPruningPruning RegimesSmoothingSpeech Recognition Accuracy
The paper presents an in-depth analysis of a less known interaction between Kneser-Ney smoothing and entropy pruning that leads to severe degradation in language model performance under aggressive pruning regimes. Experiments in a data-rich setup such as google.com voice search show a significant impact in WER as well: pruning Kneser-Ney and Katz models to 0.1 % of their original impacts speech recognition accuracy significantly, approx. 10 % relative. 1.
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SRILM - an extensible language modeling toolkit
Andreas Stolcke · 2002 · 4.4K citations
Improved backing-off for M-gram language modeling
Reinhard Kneser, Hermann Ney · 2002 · 1.5K citations