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corpus speech 0.002946358
speech recognition 0.002683143
automatic speech 0.002610303
test speech 0.002609947
native speech 0.002503731
learner speech 0.002493136
pitch accent 0.0022867499999999997
training data 0.002114308
speech 0.00211422
automatic pitch 0.001845773
pitch measures 0.001820759
binary pitch 0.0017944039999999999
pitch range 0.001747124
pitch values 0.001725536
ing data 0.0017214069999999999
test data 0.0017131569999999999
pitch target 0.0017088469999999999
pitch peak 0.00169465
pitch fea 0.0016899179999999999
last pitch 0.0016872179999999999
acoustic model 0.0016868859999999999
pitch maximum 0.001682002
pitch accents 0.001677557
pitch means 0.001676618
overall pitch 0.001665593
competitive pitch 0.0016641199999999998
pitch contour 0.001633669
actual pitch 0.001627059
pitch maxima 0.001622885
delta pitch 0.001622722
matic pitch 0.001619127
pitch height 0.001618306
pitch slope 0.001617198
prosodic recognition 0.001614943
context model 0.001614534
pitch slopes 0.001611964
absolute pitch 0.001611964
group data 0.001521437
feature set 0.001511349
prosodic annotation 0.0015068809999999998
accent recognition 0.001505983
adaptation data 0.001498298
learning prosody 0.001488762
prosody learning 0.001488762
syllable level 0.0014707420000000001
local features 0.001460602
prosody training 0.0014480360000000002
prosodic errors 0.001372416
contextual information 0.001350975
pitch 0.00134969
accent labeling 0.001346375
approximation model 0.0013391710000000001
training examples 0.001322856
prosodic labelling 0.001317284
prosodic units 0.0013115639999999999
prosodic ele 0.0013088569999999999
contextual feature 0.001297689
native training 0.001286389
feature sets 0.001281483
training sets 0.0012756780000000001
learning setting 0.00126398
guage learning 0.001254147
accent recog 0.001245308
typical language 0.0012207540000000001
learning frameworks 0.001201654
group training 0.001200885
language learners 0.001184087
feature vectors 0.001179842
language background 0.001178251
language laboratory 0.001175742
eign language 0.001174355
language instruction 0.001172445
following syllable 0.001165044
first set 0.001153708
leap corpus 0.001132353
recognition task 0.001127292
other research 0.001120326
features 0.00110911
different sources 0.0011032519999999999
first prosody 0.0010962
syllable duration 0.001079961
model 0.00106697
similar acoustic 0.001064941
prosodic 0.00104602
second set 0.001044496
preceding syllable 0.0010345929999999999
lowing syllable 0.0010330930000000001
recognition accuracy 0.001027719
skill level 0.001020506
second prosody 9.86988E-4
acoustic analysis 9.83947E-4
information 9.55969E-4
recognition approach 9.542470000000001E-4
automatic feedback 9.45527E-4
accented syllables 9.41023E-4
future work 9.4016E-4
learning 9.37604E-4
accent 9.3706E-4
recognition experiments 9.23176E-4
