speaker gender 0.002664732
same gender 0.002483734
gender classification 0.00247218
gender conversations 0.0024043899999999997
modeling gender 0.002360127
mixed gender 0.002238336
gender detection 0.002232702
gender classifica 0.00220273
different features 0.00219001
gender 0.00201039
sociolinguistic features 0.001912833
important features 0.001808701
bigram features 0.001791634
ngram features 0.001791132
sociolingusic features 0.001781908
words feature 0.001770838
features 0.00158917
language models 0.001453126
such speaker 0.001449546
feature set 0.001426304
lexical model 0.0014171890000000001
svm model 0.001321897
new language 0.0012672080000000001
general model 0.001240236
linguistic data 0.001228431
model performance 0.001219967
original model 0.0011899810000000001
tive language 0.001187236
linear model 0.001182227
regression model 0.001168587
female male 0.001152156
native language 0.0011483980000000001
arabic language 0.001141066
word usage 0.0011201100000000001
conditioned language 0.001117768
language understanding 0.001114734
data consortium 0.001112207
other attributes 0.0011087570000000001
ngram model 0.001076562
single speaker 0.001049704
classifier accuracy 0.001047493
filler words 0.0010466030000000001
such differences 0.001021671
speaker attributes 0.001021477
such properties 0.001016203
classification accuracy 0.001012307
filler word 0.001010671
test set 0.001008438
male name 9.95523E-4
conversation corpus 9.87651E-4
training set 9.79865E-4
other unigrams 9.705390000000001E-4
speaker rate 9.26729E-4
feature 9.26499E-4
latent speaker 9.180530000000001E-4
language 9.1312E-4
speaker properties 8.75341E-4
model 8.746E-4
user authenticaion 8.68614E-4
partner speaker 8.65495E-4
ing work 8.57455E-4
true speaker 8.49521E-4
words 8.44339E-4
high accuracy 8.359280000000001E-4
corpus details 8.35442E-4
standard text 8.27142E-4
related work 8.263540000000001E-4
morphological fea 8.26285E-4
tional corpus 8.215399999999999E-4
training example 8.17067E-4
arabic corpus 8.03022E-4
computational models 7.97559E-4
fisher corpus 7.91562E-4
switchboard corpus 7.907669999999999E-4
male fisher 7.86529E-4
corpus husband 7.73107E-4
velopment corpus 7.67642E-4
substantial work 7.63854E-4
lexical choice 7.56284E-4
linear svm 7.549239999999999E-4
utterance length 7.535560000000001E-4
svm framework 7.500499999999999E-4
speech transcripts 7.475559999999999E-4
rich set 7.38734E-4
prior work 7.34043E-4
mean utterance 7.26291E-4
same dis 7.230509999999999E-4
linear kernel 7.22231E-4
new attributes 7.21223E-4
modeling attributes 7.168719999999999E-4
relative use 7.11032E-4
empirical approach 7.088800000000001E-4
test conditions 7.01843E-4
training vectors 6.87756E-4
latent information 6.86762E-4
conversational speech 6.86628E-4
speech recogntition 6.84027E-4
conversation transcripts 6.711639999999999E-4
further performance 6.6648E-4
sociolinguistic studies 6.657E-4
