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model priors 0.004547425
trigram model 0.004545944
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natural language 0.00142302
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training data 0.001407115
speech corpus 0.001404882
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data problem 0.001196246
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verbmobil corpus 0.0011543319999999999
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large set 0.001128497
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sparse data 0.001011514
dialogue data 0.001010084
additional test 9.92674E-4
same length 9.77726E-4
final states 9.47909E-4
syntactic category 9.42469E-4
words 9.41639E-4
output probabilities 9.327280000000001E-4
other techniques 9.318289999999999E-4
same size 9.315510000000001E-4
output sequence 9.26318E-4
corpus 9.20588E-4
final state 9.187099999999999E-4
syntactic lasses 9.16032E-4
speech corpora 9.13967E-4
similar distribution 9.137220000000001E-4
bigrams states 9.11764E-4
same class 9.115E-4
syntactic categori 9.09602E-4
start state 9.01761E-4
state transitions 8.98623E-4
models 8.8725E-4
cessor states 8.7624E-4
test part 8.73772E-4
merging algorithm 8.73482E-4
standard speech 8.72496E-4
old states 8.71543E-4
now states 8.71543E-4
test sequence 8.70192E-4
same category 8.6572E-4
several steps 8.6488E-4
merged state 8.584319999999999E-4
end state 8.57453E-4
standard bigram 8.53828E-4
log perplexity 8.4921E-4
time complexity 8.48343E-4
