user utterances 0.0034580599999999998
user models 0.003028318
individual user 0.002749003
dialogue confidence 0.002740947
user study 0.0027176839999999997
dialogue systems 0.002699678
user identi 0.002670497
dialogue patterns 0.002534703
dialogue completion 0.002504379
tain dialogue 0.002490464
ken dialogue 0.002490464
formation system 0.002474307
system developer 0.0024489679999999997
different information 0.002370882
speech recognition 0.002250551
dialogue 0.00222967
system 0.00218726
input information 0.001938325
human speech 0.0019300899999999998
other utterances 0.001786883
speaker information 0.001785418
automatic speech 0.001740782
speech signals 0.001684786
such asr 0.0016668429999999999
other features 0.001654893
information 0.00151643
previous utterances 0.001505722
following utterances 0.001487412
asr accuracy 0.001458846
various utterances 0.001441441
asr confidence 0.001394517
asr errors 0.0013855159999999998
asr results 0.001384727
current asr 0.001377292
original utterances 0.0013741909999999999
machine learning 0.001347441
current utterance 0.0013458419999999999
speech 0.00134006
erroneous utterances 0.00131397
correct asr 0.0012627979999999999
such systems 0.001253611
learning algo 0.001245917
utterance history 0.0012022719999999999
accuracy number 0.00120104
asr acc 0.001171315
asr accuracies 0.0011516669999999999
asr accu 0.001148393
estimated asr 0.001143584
timated asr 0.001143584
mixture model 0.0011375369999999999
utterance fragments 0.001135094
target data 0.001122905
vious utterance 0.0011165209999999999
individual users 0.001098339
expert users 0.001088814
other content 0.001087677
novice users 0.001083227
such disfluencies 0.001049199
utterances 0.00104842
frequent users 0.001037633
other reason 0.001004713
high accuracy 0.001002337
much time 0.001001354
prediction accuracy 9.91499E-4
negative responses 9.83747E-4
learning 9.83408E-4
maximum number 9.49676E-4
experimental evaluation 9.23533E-4
total number 9.219860000000001E-4
experimental results 9.190380000000001E-4
features 9.1643E-4
recognition 9.10491E-4
accuracy figure 9.0442E-4
city bus 8.999329999999999E-4
interpretation error 8.92697E-4
important input 8.767149999999999E-4
prediction performance 8.628209999999999E-4
ing way 8.587950000000001E-4
error detection 8.540430000000001E-4
utterance 8.5179E-4
future work 8.457030000000001E-4
erroneous responses 8.412739999999999E-4
affirmative responses 8.38036E-4
firmative responses 8.38036E-4
response type 8.33922E-4
model 8.33157E-4
confidence measures 8.28312E-4
interpretation errors 8.27373E-4
tion performance 8.211869999999999E-4
critical problems 8.155230000000001E-4
explicit con 8.088220000000001E-4
rate figure 7.95687E-4
several conditions 7.95582E-4
general public 7.804590000000001E-4
many researchers 7.78543E-4
confidence mea 7.729869999999999E-4
terpretation errors 7.682279999999999E-4
regression function 7.67577E-4
explicit confirmations 7.652959999999999E-4
users 7.58976E-4
