Unanswerable
NONE
609
ABSTRACTIVE
FLOAT SELECTED: Table 1: Speech datasets used. Note that HAPD, HAFP and FP only have samples from healthy subjects. Detailed description in App. 2. All datasets shown in Tab. SECREF2 were transcribed manually by trained transcriptionists, employing the same list of annotations and protocols, with the same set of features extracted from the transcripts (see Sec. SECREF3 ). HAPD and HAFP are jointly referred to as HA. Binary classification of each speech transcript as AD or HC is performed. We do 5-fold cross-validation, stratified by subject so that each subject's samples do not occur in both training and testing sets in each fold. The minority class is oversampled in the training set using SMOTE BIBREF14 to deal with the class imbalance. We consider a Random Forest (100 trees), Naïve Bayes (with equal priors), SVM (with RBF kernel), and a 2-layer neural network (10 units, Adam optimizer, 500 epochs) BIBREF15 . Additionally, we augment the DB data with healthy samples from FP with varied ages. We augment DB with healthy samples from FP with varying ages (Tab. SECREF11 ), considering 50 samples for each 15 year duration starting from age 30. Adding the same number of samples from bins of age greater than 60 leads to greater increase in performance. This could be because the average age of participants in the datasets (DB, HA etc.) we use are greater than 60. Note that despite such a trend, addition of healthy data produces fair classifiers with respect to samples with age INLINEFORM0 60 and those with age INLINEFORM1 60 (balanced F1 scores of 75.6% and 76.1% respectively; further details in App. SECREF43 .) FLOAT SELECTED: Table 3: Augmenting DB with healthy data of varied ages. Scores averaged across 4 classifiers.
1811.12254
Introduction </s> Al z heimer âĢ Ļ s Ġdisease Ġ( AD ) Ġis Ġa Ġneuro deg ener ative Ġdisease Ġaffecting Ġover Ġ40 Ġmillion Ġpeople Ġworldwide Ġwith Ġhigh Ġcosts Ġof Ġacute Ġand Ġlong - term Ġcare ĠB IB REF 0 Ġ. ĠRec ruit ment Ġof Ġparticipants Ġwith Ġcognitive Ġimpairment Ġhas Ġhistorically Ġbeen Ġa Ġbottleneck Ġin Ġclinical Ġtrials ĠB IB REF 1 Ġ, Ġmaking ĠAD Ġdatasets Ġrelatively Ġsmall . ĠAdditionally , Ġthough Ġcognitive Ġassessments Ġtest Ġdomains Ġof Ġcognition Ġthrough Ġmultiple Ġtasks , Ġmost Ġavailable Ġdatasets Ġof Ġpathological Ġspeech Ġare Ġrestricted Ġto Ġparticipants Ġperforming Ġa Ġsingle Ġtask . ĠPicture Ġdescription Ġusing Ġan Ġimage Ġto Ġelicit Ġnarrative Ġdiscourse Ġsamples Ġis Ġone Ġsuch Ġtask Ġthat Ġhas Ġproved Ġto Ġbe Ġsuccessful Ġin Ġdetecting ĠAD ĠB IB REF 2 Ġ. ĠHowever , Ġit Ġis Ġimportant Ġto Ġdevelop ĠML Ġmodels Ġof Ġhigh Ġperformance Ġthat Ġwould Ġproduce Ġresults Ġgeneral izable Ġacross Ġdifferent Ġtasks . </s> Several Ġstudies Ġhave Ġused Ġnatural Ġlanguage Ġprocessing Ġand Ġmachine Ġlearning Ġto Ġdistinguish Ġbetween Ġhealthy Ġand Ġcognitive ly Ġimpaired Ġspeech Ġof Ġparticipants Ġdescribing Ġa Ġpicture . ĠFraser Ġet Ġal . ĠB IB REF 3 Ġused Ġlinguistic Ġand Ġacoustic Ġfeatures Ġto Ġclassify Ġhealthy Ġand Ġpathological Ġspeech Ġtranscripts Ġwith Ġan Ġaccuracy Ġof ĠIN LINE FORM 0 Ġ. ĠSimilarly , ĠKar le kar Ġet Ġal . ĠB IB REF 4 Ġclassified Ġutter ances Ġof Ġspeakers Ġas ĠAD Ġor Ġhealthy Ġ( HC ) Ġwith Ġan Ġaccuracy Ġof ĠIN LINE FORM 1 Ġusing Ġan Ġenlarged , Ġutter ance - level Ġview Ġof Ġtranscripts Ġof Ġpicture Ġdescriptions . ĠIn Ġline Ġwith Ġprevious Ġresearch , Ġwe Ġuse Ġlinguistic Ġand Ġacoustic Ġfeatures Ġof Ġspeech Ġas Ġinput Ġto Ġour ĠML Ġmodel . ĠFurthermore , Ġwe Ġextend Ġthe Ġmodel Ġto Ġusing Ġdata Ġfrom Ġseveral Ġdifferent Ġtasks . </s> N oor ian Ġet Ġal . ĠB IB REF 5 Ġdemonstrated Ġthat Ġusing Ġwithin - task Ġdata Ġof Ġhealthy Ġparticipants Ġdescribing Ġa Ġpicture Ġimproved ĠAD Ġdetection Ġperformance Ġby Ġup Ġto Ġ13 %. ĠIn Ġthis Ġpaper , Ġwe Ġevaluate Ġif Ġmodel Ġperformance Ġimproves Ġwith Ġthe Ġaddition Ġof Ġdata Ġfrom Ġhealthy Ġparticipants , Ġwith Ġvarying Ġages , Ġperforming Ġeither Ġthe Ġsame Ġor Ġdifferent Ġtasks . ĠWe Ġfind Ġthat Ġmodels Ġtrained Ġon Ġdatasets Ġof Ġpicture Ġdescription Ġtasks Ġaugmented Ġwith Ġconvers ational Ġspeech Ġof Ġhealthy Ġspeakers Ġlearn Ġdecision Ġboundaries Ġthat Ġare Ġmore Ġgeneral izable Ġacross Ġactivities Ġwith Ġlower Ġout - of - task Ġerrors . ĠWe Ġobserve Ġa Ġ9 % Ġincrease Ġin ĠAD Ġdetection Ġperformance Ġwhen Ġnormative Ġdata Ġfrom Ġdifferent Ġtasks Ġare Ġutilized . ĠWe Ġalso Ġanalyze Ġif Ġeach Ġtask Ġprovides Ġdomain - specific Ġinduct ive Ġbias Ġfor Ġother Ġtasks Ġto Ġobtain Ġa Ġmodel Ġsetting Ġcapable Ġof Ġdetecting ĠAD Ġfrom Ġany Ġsample Ġof Ġspeech Ġusing Ġhigh - pre cision Ġmodel - agn ostic Ġexplanations Ġproposed Ġby ĠRib e iro Ġet Ġal . ĠB IB REF 6 Ġand Ġcomputation Ġof Ġvarious Ġerror Ġmetrics Ġrelated Ġto Ġclassification . </s> Data </s> All Ġdatasets Ġshown Ġin ĠTab . ĠSEC REF 2 Ġwere Ġtransc ribed Ġmanually Ġby Ġtrained Ġtranscription ists , Ġemploying Ġthe Ġsame Ġlist Ġof Ġannotations Ġand Ġprotocols , Ġwith Ġthe Ġsame Ġset Ġof Ġfeatures Ġextracted Ġfrom Ġthe Ġtranscripts Ġ( see ĠSec . ĠSEC REF 3 Ġ). ĠH APD Ġand ĠHA FP Ġare Ġjointly Ġreferred Ġto Ġas ĠHA . </s> Methods </s> Feature ĠExt raction : ĠWe Ġextract Ġ297 Ġlinguistic Ġfeatures Ġfrom Ġthe Ġtranscripts Ġand Ġ183 Ġacoustic Ġfeatures Ġfrom Ġthe Ġassociated Ġaudio Ġfiles , Ġall Ġtask - independent . ĠL ingu istic Ġfeatures Ġencompass Ġsynt actic Ġfeatures Ġ( e . g . Ġsynt actic Ġcomplexity ĠB IB REF 9 Ġ), Ġlex ical Ġfeatures Ġ( e . g . Ġoccurrence Ġof Ġproduction Ġrules ). ĠAc oustic Ġfeatures Ġinclude ĠMel - frequency ĠC ep st ral ĠCo efficients Ġ( MF CC s ) Ġ& Ġpause - related Ġfeatures Ġ( e . g ., Ġmean Ġpause Ġduration ). ĠWe Ġalso Ġuse Ġsentiment Ġlex ical Ġnorms ĠB IB REF 10 Ġ, Ġlocal , Ġand Ġglobal Ġco herence Ġfeatures ĠB IB REF 11 Ġ. </s> Feature ĠPred icates Ġas ĠAnch ors Ġfor ĠPrediction : ĠGiven Ġa Ġblack Ġbox Ġclass ifier ĠIN LINE FORM 0 Ġwith Ġinterpret able Ġinput Ġrepresentation , ĠRib e iro Ġet Ġal . ĠB IB REF 6 Ġdefine Ġanchors ĠIN LINE FORM 1 Ġas Ġa Ġset Ġof Ġinput Ġrules Ġsuch Ġthat Ġwhen Ġconditions Ġin Ġthe Ġrule Ġare Ġmet , Ġhumans Ġcan Ġconfidently Ġpredict Ġthe Ġbehavior Ġof Ġa Ġmodel Ġwith Ġhigh Ġprecision . ĠSince Ġthe Ġinputs Ġto Ġthe Ġclass ifier Ġare Ġengineered Ġfeatures Ġwith Ġfinite Ġranges , we Ġcan Ġobtain Ġsufficient Ġconditions Ġfor Ġthe Ġprediction ĠIN LINE FORM 2 Ġin Ġterms Ġof Ġinterpret able Ġfeature Ġthresholds Ġfor Ġan Ġunseen Ġinstance ĠIN LINE FORM 3 Ġ. ĠAnch ors Ġare Ġfound Ġby Ġmaximizing Ġthe Ġmetric Ġof Ġcoverage , Ġdefined Ġas Ġthe Ġprobability Ġof Ġanchors Ġholding Ġtrue Ġto Ġsamples Ġin Ġthe Ġdata Ġdistribution ĠIN LINE FORM 4 Ġ, Ġin ĠB IB REF 6 Ġ. ĠHence , ĠIN LINE FORM 5 Ġis Ġmaxim ized , Ġwhere ĠIN LINE FORM 6 Ġ. </s> We Ġshow Ġin ĠSec . ĠSEC REF 13 Ġthat Ġanchors Ġidentified Ġfrom Ġa Ġmodel Ġtrained Ġon Ġmultiple Ġtasks Ġhave Ġmore Ġcoverage Ġover Ġthe Ġdata Ġdistribution Ġthan Ġthose Ġobtained Ġfrom Ġa Ġmodel Ġtrained Ġon Ġa Ġsingle Ġtask . ĠSuch Ġa Ġscenario Ġis Ġpossible Ġwhen Ġtask - ind epend ant , Ġclinically Ġrelevant Ġspeech Ġfeatures Ġare Ġselected Ġas Ġanchors Ġ( e . g ., Ġfraction Ġof Ġfilled Ġpauses Ġin Ġspeech ĠB IB REF 12 Ġ, Ġacoustic Ġfeatures ĠB IB REF 13 Ġetc . Ġ). ĠAdditionally , Ġsuch Ġselected Ġanchors Ġmust Ġalso Ġbe Ġassociated Ġwith Ġthresholds Ġapplicable Ġacross Ġmultiple Ġtypes Ġof Ġspeech . </s> Exper iments </s> B inary Ġclassification Ġof Ġeach Ġspeech Ġtranscript Ġas ĠAD Ġor ĠHC Ġis Ġperformed . ĠWe Ġdo Ġ5 - fold Ġcross - valid ation , Ġstrat ified Ġby Ġsubject Ġso Ġthat Ġeach Ġsubject 's Ġsamples Ġdo Ġnot Ġoccur Ġin Ġboth Ġtraining Ġand Ġtesting Ġsets Ġin Ġeach Ġfold . ĠThe Ġminority Ġclass Ġis Ġovers am pled Ġin Ġthe Ġtraining Ġset Ġusing ĠSM OTE ĠB IB REF 14 Ġto Ġdeal Ġwith Ġthe Ġclass Ġimbalance . ĠWe Ġconsider Ġa ĠRandom ĠForest Ġ( 100 Ġtrees ), ĠNa Ã¯ve ĠBay es Ġ( with Ġequal Ġpri ors ), ĠS VM Ġ( with ĠRB F Ġkernel ), Ġand Ġa Ġ2 - layer Ġneural Ġnetwork Ġ( 10 Ġunits , ĠAdam Ġoptim izer , Ġ500 Ġepoch s ) ĠB IB REF 15 Ġ. ĠAdditionally , Ġwe Ġaugment Ġthe ĠDB Ġdata Ġwith Ġhealthy Ġsamples Ġfrom ĠFP Ġwith Ġvaried Ġages . </s> Results Ġand ĠDiscussion </s> Visual ization Ġof ĠClass ĠBound aries </s> Since Ġdata Ġof Ġdifferent Ġtasks Ġhave Ġdifferent Ġnoise Ġpatterns , Ġthe Ġprobability Ġof Ġover fitting Ġto Ġnoise Ġis Ġreduced Ġwith Ġsamples Ġfrom Ġdifferent Ġtasks . ĠThis Ġcan Ġalso Ġbe Ġvisual ized Ġas Ġdecision Ġboundaries Ġof Ġmodels Ġtrained Ġon Ġvarious Ġdataset Ġcombinations . ĠFor ĠFig . ĠSEC REF 2 Ġ, Ġwe Ġembed Ġthe Ġ480 - dimensional Ġfeature Ġvector Ġinto Ġ2 Ġdimensions Ġusing ĠLoc ally ĠLinear ĠEmb edd ings ĠB IB REF 16 Ġtrained Ġon ĠDB . </s> In Ġdatasets Ġconsisting Ġof Ġpicture Ġdescriptions Ġand Ġconvers ational Ġspeech Ġ( DB Ġ+ ĠFP ), Ġthe Ġfeature Ġranges Ġincrease Ġas Ġcompared Ġto Ġpicture Ġdescription Ġtasks , Ġso Ġit Ġis Ġexpected Ġthat Ġa Ġclass ifier Ġtrained Ġon Ġstructured Ġtasks Ġonly Ġ( DB Ġ+ ĠHA FP ) Ġwould Ġincorrectly Ġclassify Ġhealthy Ġsamples Ġin Ġthe Ġfourth Ġquad rant Ġ( error Ġrates Ġfor Ġtasks Ġnot Ġin Ġdataset Ġis Ġ17 . 8 %). ĠHowever , Ġdecision Ġboundaries Ġfor Ġmodels Ġtrained Ġon Ġa Ġmix Ġof Ġstructured Ġtasks Ġand Ġun struct ured Ġconvers ational Ġspeech Ġseem Ġto Ġbe Ġmore Ġgeneral izable Ġacross Ġtasks . ĠE . g ., Ġdecision Ġboundaries Ġobtained Ġfrom ĠDB Ġ+ ĠFP Ġcould Ġapply Ġto Ġmost Ġdat ap oint s Ġin ĠHA FP Ġ( out Ġof Ġtask Ġerror Ġrate Ġis Ġ3 . 6 %). ĠClin ically , Ġsome Ġof Ġthe Ġfeatures Ġused Ġsuch Ġas Ġthe Ġpatterns Ġin Ġusage Ġof Ġfunction Ġwords Ġlike Ġpronouns Ġhave Ġshown Ġto Ġreflect Ġstress - related Ġchanges Ġin Ġgene Ġexpression , Ġpossibly Ġcaused Ġdue Ġto Ġdementia ĠB IB REF 17 Ġwhich Ġwould Ġnot Ġdepend Ġon Ġthe Ġtask Ġtype Ġand Ġcould Ġexplain Ġsuch Ġa Ġcommon Ġunderlying Ġstructure Ġto Ġfeatures . </s> Class ification ĠPerformance </s> Results Ġof Ġbinary Ġclassification Ġwith Ġdifferent Ġdataset Ġcombinations Ġ( i . e ., Ġthe Ġproportion Ġof Ġeach Ġdataset Ġused ) Ġare Ġin ĠTab . ĠSEC REF 7 Ġ. ĠThe Ġhighest ĠF 1 Ġscore Ġon ĠDB Ġis ĠIN LINE FORM 0 Ġwith ĠS VM Ġas Ġobtained Ġby ĠNo orian Ġet Ġal . ĠB IB REF 5 Ġ, Ġenabling Ġsimilar Ġcomparisons . </s> We Ġsee Ġthe Ġsame Ġtrend Ġof Ġincreasing Ġmodel Ġperformance Ġwith Ġnormative Ġdata Ġfrom Ġthe Ġpicture Ġdescription Ġtask , Ġas Ġshown Ġby ĠNo orian Ġet Ġal . ĠB IB REF 5 Ġ. ĠWe Ġobserve Ġthat Ġthis Ġincrease Ġis Ġindependent Ġof Ġthe Ġnature Ġof Ġthe Ġtask Ġperformed ĠâĢĵ Ġnormative Ġpicture Ġdescription Ġtask Ġdata Ġof Ġsimilar Ġsize Ġas Ġin ĠB IB REF 5 Ġand Ġthe Ġsame Ġamount Ġof Ġnormative Ġdata Ġfrom Ġdifferent Ġstructured Ġtasks Ġof Ġflu ency Ġtests Ġand Ġparagraph Ġreading Ġprove Ġto Ġbe Ġhelpful , Ġbringing Ġabout Ġa Ġsimilar Ġincrease Ġin Ġscores Ġ(+ 2 %, Ġ+ 5 % Ġabsolute ĠF 1 Ġmicro Ġand Ġmacro ). ĠInterestingly , Ġperformance Ġof Ġdetecting Ġthe Ġmajority Ġ( healthy ) Ġclass Ġ( ref lected Ġin ĠF 1 Ġmicro ) Ġas Ġwell Ġas Ġthe Ġminority Ġ( AD ) Ġclass Ġ( ref lected Ġin ĠF 1 Ġmacro ) Ġincreases Ġwith Ġadditional Ġdata . </s> Aug ment ing ĠDB Ġwith Ġsame Ġamount Ġof Ġsamples Ġfrom Ġstructured Ġtasks Ġ( HA ) Ġand Ġfrom Ġconvers ational Ġspeech Ġ( FP ) Ġbrings Ġabout Ġsimilar Ġperformance . ĠDou bling Ġthe Ġinitial Ġamount Ġof Ġcontrol Ġdata Ġwith Ġdata Ġfrom Ġa Ġdifferent Ġstructured Ġtask Ġ( HA , ĠHA FP ) Ġresults Ġin Ġan Ġincrease Ġof Ġup Ġto Ġ9 % Ġin ĠF 1 Ġscores . </s> Imp act Ġof ĠAge </s> We Ġaugment ĠDB Ġwith Ġhealthy Ġsamples Ġfrom ĠFP Ġwith Ġvarying Ġages Ġ( Tab . ĠSEC REF 11 Ġ), Ġconsidering Ġ50 Ġsamples Ġfor Ġeach Ġ15 Ġyear Ġduration Ġstarting Ġfrom Ġage Ġ30 . ĠAdding Ġthe Ġsame Ġnumber Ġof Ġsamples Ġfrom Ġbins Ġof Ġage Ġgreater Ġthan Ġ60 Ġleads Ġto Ġgreater Ġincrease Ġin Ġperformance . ĠThis Ġcould Ġbe Ġbecause Ġthe Ġaverage Ġage Ġof Ġparticipants Ġin Ġthe Ġdatasets Ġ( DB , ĠHA Ġetc .) Ġwe Ġuse Ġare Ġgreater Ġthan Ġ60 . ĠNote Ġthat Ġdespite Ġsuch Ġa Ġtrend , Ġaddition Ġof Ġhealthy Ġdata Ġproduces Ġfair Ġclass ifiers Ġwith Ġrespect Ġto Ġsamples Ġwith Ġage ĠIN LINE FORM 0 Ġ60 Ġand Ġthose Ġwith Ġage ĠIN LINE FORM 1 Ġ60 Ġ( balanced ĠF 1 Ġscores Ġof Ġ75 . 6 % Ġand Ġ76 . 1 % Ġrespectively ; Ġfurther Ġdetails Ġin ĠApp . ĠSEC REF 43 Ġ.) </s> Ind uctive ĠB ias Ġof ĠT asks </s> Each Ġtask Ġperformed Ġin Ġthe Ġdatasets Ġis Ġdesigned Ġto Ġassess Ġdifferent Ġcognitive Ġfunctions , Ġe . g . Ġflu ency Ġtask Ġis Ġused Ġto Ġevaluate Ġthe Ġability Ġto Ġorganize Ġand Ġplan ĠB IB REF 18 Ġand Ġpicture Ġdescription Ġtask ĠâĢĵ Ġfor Ġdetecting Ġdiscourse - related Ġimpair ments ĠB IB REF 19 Ġ. ĠAs Ġa Ġresult , Ġit Ġis Ġexpected Ġthat Ġthe Ġnature Ġof Ġdecision Ġfunctions Ġand Ġfeature Ġpred icates Ġlearned Ġon Ġdata Ġof Ġeach Ġof Ġthese Ġtasks Ġwould Ġbe Ġdifferent . ĠPerformance Ġof ĠAD Ġidentification Ġwith Ġaddition Ġof Ġnormative Ġdata Ġfrom Ġmultiple Ġtasks Ġ( Tab . ĠSEC REF 7 Ġ), Ġdespite Ġthe Ġpossibly Ġdifferent Ġnature Ġof Ġdecision Ġfunctions , Ġsuggests Ġthat Ġtraining Ġthe Ġmodel Ġwith Ġsamples Ġfrom Ġeach Ġtask Ġprovides Ġdomain - specific Ġinduct ive Ġbias Ġfor Ġother Ġtasks . ĠWe Ġstudy Ġpossible Ġunderlying Ġmechanisms Ġresponsible Ġfor Ġthis , Ġsuggested Ġby ĠCar uana Ġet Ġal . ĠB IB REF 20 Ġand ĠRud er Ġet Ġal . ĠB IB REF 21 Ġ. </s> Att ention - f ocusing Ġon ĠRe levant ĠFeatures : ĠRud er Ġet Ġal . ĠB IB REF 21 Ġclaim Ġthat Ġin Ġa Ġsmall , Ġhigh - dimensional Ġdataset , Ġinformation Ġregarding Ġrelevance Ġor Ġirre lev ance Ġof Ġparticular Ġfeatures Ġis Ġdifficult Ġto Ġcapture . ĠHowever , Ġdata Ġrelated Ġto Ġmultiple Ġtasks Ġcan Ġhelp Ġidentify Ġfeatures Ġrelevant Ġacross Ġdifferent Ġactivities . ĠWe Ġcan Ġuse Ġanchor Ġvariables ĠB IB REF 6 Ġto Ġshow Ġthis Ġeffect . ĠThe Ġcoverage Ġof Ġfeatures Ġanch oring Ġthe Ġprediction Ġof Ġan Ġinstance Ġindicates Ġthe Ġapplic ability Ġof Ġthe Ġfeature Ġpredicate Ġto Ġthe Ġrest Ġof Ġthe Ġdata Ġdistribution Ġand Ġhence Ġthe Ġimportance Ġof Ġthe Ġfeature Ġacross Ġthe Ġdata Ġdistribution . ĠThe Ġcoverage Ġof Ġthe Ġanchors Ġselected Ġfor Ġa Ġtest Ġset Ġwhich Ġis Ġ10 % Ġ( 50 Ġsamples ) Ġof ĠDB Ġchanges Ġby Ġ40 . 8 % Ġ( from Ġ0 . 05 Ġto Ġ0 . 07 ) Ġon Ġthe Ġaddition Ġof Ġthe ĠHA , Ġwhich Ġindicates Ġthat Ġthere Ġis Ġan Ġattention Ġfocusing Ġeffect . </s> Represent ation Ġbias : ĠAs Ġshown Ġby ĠSch ulz Ġet Ġal . ĠB IB REF 22 Ġ, Ġmodels Ġtrained Ġon Ġdata Ġfrom Ġmultiple Ġtasks Ġperform Ġbetter Ġthan Ġwith Ġsingle - task Ġinformation Ġwhen Ġlittle Ġtraining Ġdata Ġis Ġavailable Ġfor Ġthe Ġmain Ġtask . ĠThe Ġnon - linear Ġtrend Ġof Ġincrease Ġin Ġmodel Ġperformance Ġwith Ġthe Ġaddition Ġof Ġdifferent Ġamounts Ġof Ġdata Ġis Ġshown Ġin ĠApp . ĠSEC REF 41 Ġ. ĠThe ĠF 1 Ġmicro Ġscore Ġof Ġthe Ġbest Ġperforming Ġmodel Ġtrained Ġon ĠDB Ġ+ ĠHA Ġis Ġ82 . 28 % Ġfor Ġpicture Ġdescription Ġtasks , Ġ95 . 4 % Ġfor Ġparagraph Ġreading Ġand Ġ97 . 01 % Ġfor Ġflu ency Ġtasks . ĠThis Ġshows Ġgreater Ġthan Ġtrivial Ġperformance Ġfor Ġeach Ġtask Ġand Ġimprovement Ġin Ġperformance Ġfor Ġpicture Ġdescription Ġtask Ġfrom Ġtraining Ġa Ġmodel Ġpurely Ġon ĠDB . ĠSuch Ġan Ġeffect Ġhelps Ġthe Ġmodel Ġachieve Ġnon - t riv ial Ġperformance Ġon ĠAD Ġdetection Ġfor Ġnovel Ġtasks Ġmeasuring Ġmultiple Ġdomains Ġof Ġcognition , Ġgiven Ġa Ġsufficiently Ġlarge Ġnumber Ġof Ġtraining Ġtasks Ġaccording Ġto Ġalgorithms Ġprovided Ġby ĠBaxter Ġet Ġal . ĠB IB REF 23 Ġ. ĠHence , Ġtraining Ġmodels Ġon Ġmany Ġspeech - based Ġtasks Ġcould Ġhelp Ġdevelop Ġan Ġalgorithm Ġcapable Ġof Ġdetecting ĠAD Ġfrom Ġany Ġsample Ġof Ġspontaneous Ġspeech . </s> O ng oing Ġwork Ġis Ġon Ġdetailed Ġanalysis Ġof Ġnature Ġand Ġpol arity Ġof Ġfeature Ġtrends Ġacross Ġvarious Ġspeech Ġtasks . ĠFuture Ġwork Ġwill Ġfocus Ġon Ġlearning Ġinterpret able Ġlatent Ġrepresentations Ġbased Ġon Ġthe Ġobservations Ġmade , Ġcapable Ġof Ġgood Ġpredictive Ġperformance Ġacross Ġa Ġmultitude Ġof Ġtasks . </s> Detailed ĠDescription Ġof ĠDat as ets </s> D ement ia Bank Ġ( DB ): ĠThe ĠD ement ia Bank Ġdataset Ġis Ġthe Ġlargest Ġavailable Ġpublic Ġdataset Ġof Ġspeech Ġfor Ġassessing Ġcognitive Ġimpair ments . ĠIt Ġconsists Ġof Ġnarrative Ġpicture Ġdescriptions Ġfrom Ġparticipants Ġaged Ġbetween Ġ45 Ġto Ġ90 ĠB IB REF 24 Ġ. ĠIn Ġeach Ġsample , Ġa Ġparticipant Ġdescribes Ġthe Ġpicture Ġthat Ġthey Ġare Ġshown . ĠOut Ġof Ġthe Ġ210 Ġparticipants Ġin Ġthe Ġstudy , Ġ117 Ġwere Ġdiagnosed Ġwith ĠAD Ġ( ĠIN LINE FORM 0 Ġsamples Ġof Ġspeech ) Ġand Ġ93 Ġwere Ġhealthy Ġ( HC ; ĠIN LINE FORM 1 Ġsamples ) Ġwith Ġmany Ġsubjects Ġrepeating Ġthe Ġtask Ġwith Ġan Ġinterval Ġof Ġa Ġyear . ĠDem ographics Ġof Ġage , Ġsex , Ġand Ġyears Ġof Ġeducation Ġare Ġprovided Ġin Ġthe Ġdataset . </s> Health y ĠAging Ġ( HA ) Ġ: ĠThe ĠHealthy ĠAging Ġdataset Ġconsists Ġof Ġspeech Ġsamples Ġof Ġcognitive ly Ġhealthy Ġparticipants Ġ( ĠIN LINE FORM 0 Ġ) Ġolder Ġthan Ġ50 Ġyears . ĠEach Ġparticipant Ġperforms Ġthree Ġstructured Ġtasks ĠâĢĵ Ġpicture Ġdescription Ġ( H APD ), Ġverbal Ġflu ency Ġtest , Ġand Ġa Ġparagraph Ġreading Ġtask . ĠFlu ency Ġand Ġparagraph Ġtasks Ġare Ġjointly Ġreferred Ġto Ġas ĠHA FP . ĠThe Ġaverage Ġnumber Ġof Ġsamples Ġper Ġparticipant Ġis Ġ14 . 46 . ĠThe Ġdataset Ġconstitutes Ġ8 . 5 Ġhours Ġof Ġtotal Ġaudio . </s> F amous ĠPeople Ġ( FP ): ĠThe ĠFamous ĠPeople Ġdataset ĠB IB REF 8 Ġconsists Ġof Ġpublicly Ġavailable Ġspontaneous Ġspeech Ġsamples Ġfrom Ġ9 Ġfamous Ġindividuals Ġ( e . g ., ĠWoody ĠAllen Ġ& ĠClint ĠEast wood ) Ġover Ġthe Ġperiod Ġfrom Ġ1956 Ġto Ġ2017 , Ġspanning Ġperiods Ġfrom Ġearly Ġadulthood Ġto Ġolder Ġage , Ġwith Ġan Ġaverage Ġof Ġ25 Ġsamples Ġper Ġperson . ĠWe Ġuse Ġspeech Ġsamples Ġof Ġthese Ġsubjects Ġwho Ġare Ġconsidered Ġto Ġbe Ġhealthy Ġ( ĠIN LINE FORM 0 Ġ), Ġgiven Ġan Ġabsence Ġof Ġany Ġreported Ġdiagnosis Ġor Ġsubjective Ġmemory Ġcomplaints . ĠThis Ġhealthy Ġcontrol Ġ( HC ) Ġgroup Ġcovers Ġa Ġvariety Ġof Ġspeaker Ġages , Ġfrom Ġ30 Ġto Ġ88 Ġ( ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ). </s> Features </s> A Ġlist Ġof Ġ480 Ġfeatures Ġbelonging Ġto Ġthree Ġgroups Ġ- Ġacoustic , Ġsemantic / Ġsynt actic Ġand Ġlex ical . ĠThese Ġfeatures Ġinclude Ġconstituency - p ars ing Ġbased Ġfeatures , Ġsynt actic Ġcomplexity Ġfeatures Ġextracted Ġusing ĠLu ĠSynt actic ĠComplex ity Ġanaly zer ĠB IB REF 9 Ġ, ĠMF CC Ġmeans , Ġvar iances Ġand Ġother Ġhigher Ġorder Ġmoments . ĠFew Ġof Ġthese Ġfeatures Ġare Ġlisted Ġbelow Ġ: </s> Ph on ation Ġrate Ġ: ĠPercentage Ġof Ġrecording Ġthat Ġis Ġvoiced . </s> Me an Ġpause Ġduration Ġ: ĠMean Ġduration Ġof Ġpauses Ġin Ġseconds . </s> Pause Ġword Ġratio Ġ: ĠRatio Ġof Ġsilent Ġsegments Ġto Ġvoiced Ġsegments . </s> Short Ġpause Ġcount Ġnormalized Ġ: ĠNormal ized Ġnumber Ġof Ġpauses Ġless Ġthan Ġ1 Ġsecond . </s> Medium Ġpause Ġcount Ġnormalized Ġ: ĠNormal ized Ġnumber Ġof Ġpauses Ġbetween Ġ1 Ġsecond Ġand Ġ2 Ġseconds Ġin Ġlength . </s> Z CR Ġk urt osis Ġ: ĠKurt osis Ġof ĠZero ĠCrossing ĠRate Ġ( Z CR ) Ġof Ġall Ġvoiced Ġsegments Ġacross Ġframes . </s> MF CC Ġmeans Ġ: ĠMean Ġof Ġvelocity Ġof ĠMF CC Ġcoefficient Ġover Ġall Ġframes Ġ( this Ġis Ġcalculated Ġfor Ġmultiple Ġcoefficients ). </s> MF CC Ġk urt osis : ĠKurt osis Ġof Ġmean Ġfeatures . </s> MF CC Ġvariance : ĠVari ance Ġof Ġacceleration Ġof Ġframe Ġenergy Ġover Ġall Ġframes . </s> Moving - average Ġtype - token Ġratio Ġ( MAT TR ): ĠMoving Ġaverage ĠT TR Ġ( type - token Ġratio ) Ġover Ġa Ġwindow Ġof Ġ10 Ġtokens . </s> Cos ine Ġcutoff Ġ: ĠF raction Ġof Ġpairs Ġof Ġutter ances Ġwith Ġcos ine Ġdistance ĠIN LINE FORM 0 Ġ0 . 001 . </s> Pa uses Ġof Ġtype Ġ` uh ' Ġ: ĠThe Ġnumber Ġof Ġ` uh ' Ġfill ers Ġover Ġall Ġtokens . </s> Numbers Ġof Ġinter ject ions / n umer als Ġ: ĠThe Ġnumber Ġof Ġinter ject ions / n umer als Ġused Ġover Ġall Ġtokens . </s> N oun Ġratio : ĠRatio Ġof Ġnumber Ġof Ġnoun s Ġto Ġnumber Ġof Ġnoun s Ġ+ Ġverbs . </s> Tem poral Ġcohesion Ġfeature Ġ: ĠAverage Ġnumber Ġof Ġswitches Ġin Ġtense . </s> Spe ech Ġgraph Ġfeatures Ġ: ĠFeatures Ġextracted Ġfrom Ġgraph Ġof Ġspoken Ġwords Ġin Ġa Ġsample Ġincluding Ġaverage Ġtotal Ġdegree , Ġnumber Ġof Ġedges , Ġaverage Ġshortest Ġpath , Ġgraph Ġdiameter Ġ( und irect ed ) Ġand Ġgraph Ġdensity . </s> F illed Ġpauses Ġ: ĠNumber Ġof Ġnon - sil ent Ġpauses . </s> N oun Ġfrequency Ġ: ĠAverage Ġfrequency Ġnorm Ġfor Ġall Ġnoun s . </s> N oun Ġimage ability : ĠAverage Ġimage ability Ġnorm Ġfor Ġall Ġnoun s . </s> Features Ġfrom Ġparse - tree Ġ: ĠNumber Ġof Ġtimes Ġproduction Ġrules Ġsuch Ġas Ġnumber Ġof Ġnoun Ġphrases Ġto Ġdetermin ers Ġoccurrences , Ġoccur Ġover Ġthe Ġtotal Ġnumber Ġof Ġproductions Ġin Ġthe Ġtranscript 's Ġparse Ġtree . </s> Sy nt actic Ġcomplexity Ġfeatures : ĠRatio Ġof Ġclauses Ġto ĠT - units , ĠRatio Ġof Ġclauses Ġto Ġsentences Ġetc . ĠB IB REF 9 </s> Hyper - param eters : </s> Ga ussian ĠNa ive ĠBay es Ġwith Ġbalanced Ġpri ors Ġis Ġused . </s> The Ġrandom Ġforest Ġclass ifier Ġfits Ġ100 Ġdecision Ġtrees Ġwith Ġother Ġdefault Ġparameters Ġin ĠB IB REF 15 Ġ. </s> S VM Ġis Ġtrained Ġwith Ġradial Ġbasis Ġfunction Ġkernel , Ġregular ization Ġparameter ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġ. </s> The ĠN N Ġconsists Ġof Ġone Ġhidden Ġlayer Ġof Ġ10 Ġunits . ĠThe Ġtan h Ġactivation Ġfunction Ġis Ġused Ġat Ġeach Ġhidden Ġlayer . ĠThe Ġnetwork Ġis Ġtrained Ġusing ĠAdam Ġfor Ġ100 Ġepoch s Ġwith Ġother Ġdefault Ġparameters Ġin ĠB IB REF 15 Ġ. </s> Effect Ġof Ġdata Ġfrom Ġdifferent Ġtasks : </s> The Ġeffect Ġof Ġaugment ing ĠDB Ġwith Ġdata Ġfrom Ġa Ġdifferent Ġstructured Ġtask Ġ( HA FP ) Ġis Ġshown Ġin ĠSEC REF 41 Ġ. </s> F 1 Ġscores Ġ( micro Ġand Ġmacro ) Ġincrease Ġnon - lin early Ġwith Ġthe Ġaddition Ġof Ġdata . </s> Fair ness Ġwith ĠRespect Ġto ĠAge : </s> We Ġevaluate Ġfairness Ġof Ġclassification Ġwith Ġrespect Ġto Ġtwo Ġgroups Ġ- Ġsamples Ġwith Ġage ĠIN LINE FORM 0 Ġ60 Ġand Ġthose Ġwith Ġage ĠIN LINE FORM 1 Ġ60 . ĠA Ġfair Ġclass ifier Ġwould Ġproduce Ġcomparable Ġclassification Ġscores Ġfor Ġboth Ġgroups . ĠFor Ġthe Ġbest Ġperforming Ġclass ifier Ġon ĠDB , Ġthe ĠF 1 Ġ( micro ) Ġscore Ġfor Ġsamples Ġwith Ġage ĠIN LINE FORM 2 Ġ60 Ġis Ġ85 . 9 % Ġand Ġwith Ġage ĠIN LINE FORM 3 Ġ60 Ġis Ġ76 . 4 %. ĠWith Ġthe Ġaddition Ġof ĠHA , Ġthe ĠF 1 Ġ( micro ) Ġscore Ġfor Ġsamples Ġwith Ġage ĠIN LINE FORM 4 Ġ60 Ġand Ġwith Ġage ĠIN LINE FORM 5 Ġis Ġmore Ġbalanced Ġ( 75 . 6 %, Ġ76 . 1 % Ġrespectively ) Ġfor Ġthe Ġsame Ġset Ġof Ġdata Ġpoints Ġfrom ĠDB . ĠNote Ġthat Ġthe Ġaverage Ġage Ġin Ġboth Ġdatasets Ġare Ġsimilar Ġ( ĠIN LINE FORM 6 Ġ).
Introduction Alzheimer’s disease (AD) is a neurodegenerative disease affecting over 40 million people worldwide with high costs of acute and long-term care BIBREF0 . Recruitment of participants with cognitive impairment has historically been a bottleneck in clinical trials BIBREF1 , making AD datasets relatively small. Additionally, though cognitive assessments test domains of cognition through multiple tasks, most available datasets of pathological speech are restricted to participants performing a single task. Picture description using an image to elicit narrative discourse samples is one such task that has proved to be successful in detecting AD BIBREF2 . However, it is important to develop ML models of high performance that would produce results generalizable across different tasks. Several studies have used natural language processing and machine learning to distinguish between healthy and cognitively impaired speech of participants describing a picture. Fraser et al. BIBREF3 used linguistic and acoustic features to classify healthy and pathological speech transcripts with an accuracy of INLINEFORM0 . Similarly, Karlekar et al. BIBREF4 classified utterances of speakers as AD or healthy (HC) with an accuracy of INLINEFORM1 using an enlarged, utterance-level view of transcripts of picture descriptions. In line with previous research, we use linguistic and acoustic features of speech as input to our ML model. Furthermore, we extend the model to using data from several different tasks. Noorian et al. BIBREF5 demonstrated that using within-task data of healthy participants describing a picture improved AD detection performance by up to 13%. In this paper, we evaluate if model performance improves with the addition of data from healthy participants, with varying ages, performing either the same or different tasks. We find that models trained on datasets of picture description tasks augmented with conversational speech of healthy speakers learn decision boundaries that are more generalizable across activities with lower out-of-task errors. We observe a 9% increase in AD detection performance when normative data from different tasks are utilized. We also analyze if each task provides domain-specific inductive bias for other tasks to obtain a model setting capable of detecting AD from any sample of speech using high-precision model-agnostic explanations proposed by Ribeiro et al. BIBREF6 and computation of various error metrics related to classification. Data All datasets shown in Tab. SECREF2 were transcribed manually by trained transcriptionists, employing the same list of annotations and protocols, with the same set of features extracted from the transcripts (see Sec. SECREF3 ). HAPD and HAFP are jointly referred to as HA. Methods Feature Extraction: We extract 297 linguistic features from the transcripts and 183 acoustic features from the associated audio files, all task-independent. Linguistic features encompass syntactic features (e.g. syntactic complexity BIBREF9 ), lexical features (e.g. occurrence of production rules). Acoustic features include Mel-frequency Cepstral Coefficients (MFCCs) & pause-related features (e.g., mean pause duration). We also use sentiment lexical norms BIBREF10 , local, and global coherence features BIBREF11 . Feature Predicates as Anchors for Prediction: Given a black box classifier INLINEFORM0 with interpretable input representation, Ribeiro et al. BIBREF6 define anchors INLINEFORM1 as a set of input rules such that when conditions in the rule are met, humans can confidently predict the behavior of a model with high precision. Since the inputs to the classifier are engineered features with finite ranges,we can obtain sufficient conditions for the prediction INLINEFORM2 in terms of interpretable feature thresholds for an unseen instance INLINEFORM3 . Anchors are found by maximizing the metric of coverage, defined as the probability of anchors holding true to samples in the data distribution INLINEFORM4 , in BIBREF6 . Hence, INLINEFORM5 is maximized, where INLINEFORM6 . We show in Sec. SECREF13 that anchors identified from a model trained on multiple tasks have more coverage over the data distribution than those obtained from a model trained on a single task. Such a scenario is possible when task-independant, clinically relevant speech features are selected as anchors (e.g., fraction of filled pauses in speech BIBREF12 , acoustic features BIBREF13 etc. ). Additionally, such selected anchors must also be associated with thresholds applicable across multiple types of speech. Experiments Binary classification of each speech transcript as AD or HC is performed. We do 5-fold cross-validation, stratified by subject so that each subject's samples do not occur in both training and testing sets in each fold. The minority class is oversampled in the training set using SMOTE BIBREF14 to deal with the class imbalance. We consider a Random Forest (100 trees), Naïve Bayes (with equal priors), SVM (with RBF kernel), and a 2-layer neural network (10 units, Adam optimizer, 500 epochs) BIBREF15 . Additionally, we augment the DB data with healthy samples from FP with varied ages. Results and Discussion Visualization of Class Boundaries Since data of different tasks have different noise patterns, the probability of overfitting to noise is reduced with samples from different tasks. This can also be visualized as decision boundaries of models trained on various dataset combinations. For Fig. SECREF2 , we embed the 480-dimensional feature vector into 2 dimensions using Locally Linear Embeddings BIBREF16 trained on DB. In datasets consisting of picture descriptions and conversational speech (DB + FP), the feature ranges increase as compared to picture description tasks, so it is expected that a classifier trained on structured tasks only (DB + HAFP) would incorrectly classify healthy samples in the fourth quadrant (error rates for tasks not in dataset is 17.8%). However, decision boundaries for models trained on a mix of structured tasks and unstructured conversational speech seem to be more generalizable across tasks. E.g., decision boundaries obtained from DB + FP could apply to most datapoints in HAFP (out of task error rate is 3.6%). Clinically, some of the features used such as the patterns in usage of function words like pronouns have shown to reflect stress-related changes in gene expression, possibly caused due to dementia BIBREF17 which would not depend on the task type and could explain such a common underlying structure to features. Classification Performance Results of binary classification with different dataset combinations (i.e., the proportion of each dataset used) are in Tab. SECREF7 . The highest F1 score on DB is INLINEFORM0 with SVM as obtained by Noorian et al. BIBREF5 , enabling similar comparisons. We see the same trend of increasing model performance with normative data from the picture description task, as shown by Noorian et al. BIBREF5 . We observe that this increase is independent of the nature of the task performed – normative picture description task data of similar size as in BIBREF5 and the same amount of normative data from different structured tasks of fluency tests and paragraph reading prove to be helpful, bringing about a similar increase in scores (+2%, +5% absolute F1 micro and macro). Interestingly, performance of detecting the majority (healthy) class (reflected in F1 micro) as well as the minority (AD) class (reflected in F1 macro) increases with additional data. Augmenting DB with same amount of samples from structured tasks (HA) and from conversational speech (FP) brings about similar performance. Doubling the initial amount of control data with data from a different structured task (HA, HAFP) results in an increase of up to 9% in F1 scores. Impact of Age We augment DB with healthy samples from FP with varying ages (Tab. SECREF11 ), considering 50 samples for each 15 year duration starting from age 30. Adding the same number of samples from bins of age greater than 60 leads to greater increase in performance. This could be because the average age of participants in the datasets (DB, HA etc.) we use are greater than 60. Note that despite such a trend, addition of healthy data produces fair classifiers with respect to samples with age INLINEFORM0 60 and those with age INLINEFORM1 60 (balanced F1 scores of 75.6% and 76.1% respectively; further details in App. SECREF43 .) Inductive Bias of Tasks Each task performed in the datasets is designed to assess different cognitive functions, e.g. fluency task is used to evaluate the ability to organize and plan BIBREF18 and picture description task – for detecting discourse-related impairments BIBREF19 . As a result, it is expected that the nature of decision functions and feature predicates learned on data of each of these tasks would be different. Performance of AD identification with addition of normative data from multiple tasks (Tab. SECREF7 ), despite the possibly different nature of decision functions, suggests that training the model with samples from each task provides domain-specific inductive bias for other tasks. We study possible underlying mechanisms responsible for this, suggested by Caruana et al. BIBREF20 and Ruder et al. BIBREF21 . Attention-focusing on Relevant Features: Ruder et al. BIBREF21 claim that in a small, high-dimensional dataset, information regarding relevance or irrelevance of particular features is difficult to capture. However, data related to multiple tasks can help identify features relevant across different activities. We can use anchor variables BIBREF6 to show this effect. The coverage of features anchoring the prediction of an instance indicates the applicability of the feature predicate to the rest of the data distribution and hence the importance of the feature across the data distribution. The coverage of the anchors selected for a test set which is 10% (50 samples) of DB changes by 40.8% (from 0.05 to 0.07) on the addition of the HA, which indicates that there is an attention focusing effect. Representation bias: As shown by Schulz et al. BIBREF22 , models trained on data from multiple tasks perform better than with single-task information when little training data is available for the main task. The non-linear trend of increase in model performance with the addition of different amounts of data is shown in App. SECREF41 . The F1 micro score of the best performing model trained on DB + HA is 82.28% for picture description tasks, 95.4% for paragraph reading and 97.01% for fluency tasks. This shows greater than trivial performance for each task and improvement in performance for picture description task from training a model purely on DB. Such an effect helps the model achieve non-trivial performance on AD detection for novel tasks measuring multiple domains of cognition, given a sufficiently large number of training tasks according to algorithms provided by Baxter et al. BIBREF23 . Hence, training models on many speech-based tasks could help develop an algorithm capable of detecting AD from any sample of spontaneous speech. Ongoing work is on detailed analysis of nature and polarity of feature trends across various speech tasks. Future work will focus on learning interpretable latent representations based on the observations made, capable of good predictive performance across a multitude of tasks. Detailed Description of Datasets DementiaBank (DB): The DementiaBank dataset is the largest available public dataset of speech for assessing cognitive impairments. It consists of narrative picture descriptions from participants aged between 45 to 90 BIBREF24 . In each sample, a participant describes the picture that they are shown. Out of the 210 participants in the study, 117 were diagnosed with AD ( INLINEFORM0 samples of speech) and 93 were healthy (HC; INLINEFORM1 samples) with many subjects repeating the task with an interval of a year. Demographics of age, sex, and years of education are provided in the dataset. Healthy Aging (HA) : The Healthy Aging dataset consists of speech samples of cognitively healthy participants ( INLINEFORM0 ) older than 50 years. Each participant performs three structured tasks – picture description (HAPD), verbal fluency test, and a paragraph reading task. Fluency and paragraph tasks are jointly referred to as HAFP. The average number of samples per participant is 14.46. The dataset constitutes 8.5 hours of total audio. Famous People (FP): The Famous People dataset BIBREF8 consists of publicly available spontaneous speech samples from 9 famous individuals (e.g., Woody Allen & Clint Eastwood) over the period from 1956 to 2017, spanning periods from early adulthood to older age, with an average of 25 samples per person. We use speech samples of these subjects who are considered to be healthy ( INLINEFORM0 ), given an absence of any reported diagnosis or subjective memory complaints. This healthy control (HC) group covers a variety of speaker ages, from 30 to 88 ( INLINEFORM1 , INLINEFORM2 ). Features A list of 480 features belonging to three groups - acoustic, semantic/ syntactic and lexical. These features include constituency-parsing based features, syntactic complexity features extracted using Lu Syntactic Complexity analyzer BIBREF9 , MFCC means, variances and other higher order moments. Few of these features are listed below : Phonation rate : Percentage of recording that is voiced. Mean pause duration : Mean duration of pauses in seconds. Pause word ratio : Ratio of silent segments to voiced segments. Short pause count normalized : Normalized number of pauses less than 1 second. Medium pause count normalized : Normalized number of pauses between 1 second and 2 seconds in length. ZCR kurtosis : Kurtosis of Zero Crossing Rate (ZCR) of all voiced segments across frames. MFCC means : Mean of velocity of MFCC coefficient over all frames (this is calculated for multiple coefficients). MFCC kurtosis: Kurtosis of mean features. MFCC variance: Variance of acceleration of frame energy over all frames. Moving-average type-token ratio (MATTR): Moving average TTR (type-token ratio) over a window of 10 tokens. Cosine cutoff : Fraction of pairs of utterances with cosine distance INLINEFORM0 0.001. Pauses of type `uh' : The number of `uh' fillers over all tokens. Numbers of interjections/numerals : The number of interjections/numerals used over all tokens. Noun ratio: Ratio of number of nouns to number of nouns + verbs. Temporal cohesion feature : Average number of switches in tense. Speech graph features : Features extracted from graph of spoken words in a sample including average total degree, number of edges, average shortest path, graph diameter (undirected) and graph density. Filled pauses : Number of non-silent pauses. Noun frequency : Average frequency norm for all nouns. Noun imageability: Average imageability norm for all nouns. Features from parse-tree : Number of times production rules such as number of noun phrases to determiners occurrences, occur over the total number of productions in the transcript's parse tree. Syntactic complexity features: Ratio of clauses to T-units, Ratio of clauses to sentences etc. BIBREF9 Hyper-parameters: Gaussian Naive Bayes with balanced priors is used. The random forest classifier fits 100 decision trees with other default parameters in BIBREF15 . SVM is trained with radial basis function kernel, regularization parameter INLINEFORM0 and INLINEFORM1 . The NN consists of one hidden layer of 10 units. The tanh activation function is used at each hidden layer. The network is trained using Adam for 100 epochs with other default parameters in BIBREF15 . Effect of data from different tasks: The effect of augmenting DB with data from a different structured task (HAFP) is shown in SECREF41 . F1 scores (micro and macro) increase non-linearly with the addition of data. Fairness with Respect to Age: We evaluate fairness of classification with respect to two groups - samples with age INLINEFORM0 60 and those with age INLINEFORM1 60. A fair classifier would produce comparable classification scores for both groups. For the best performing classifier on DB, the F1 (micro) score for samples with age INLINEFORM2 60 is 85.9% and with age INLINEFORM3 60 is 76.4%. With the addition of HA, the F1 (micro) score for samples with age INLINEFORM4 60 and with age INLINEFORM5 is more balanced (75.6%, 76.1% respectively) for the same set of data points from DB. Note that the average age in both datasets are similar ( INLINEFORM6 ).
what is the size of the augmented dataset?
57f23dfc264feb62f45d9a9e24c60bd73d7fe563
what Ġis Ġthe Ġsize Ġof Ġthe Ġaugmented Ġdataset ?
Un answer able
<s> what Ġis Ġthe Ġsize Ġof Ġthe Ġaugmented Ġdataset ? </s> Introduction </s> Al z heimer âĢ Ļ s Ġdisease Ġ( AD ) Ġis Ġa Ġneuro deg ener ative Ġdisease Ġaffecting Ġover Ġ40 Ġmillion Ġpeople Ġworldwide Ġwith Ġhigh Ġcosts Ġof Ġacute Ġand Ġlong - term Ġcare ĠB IB REF 0 Ġ. ĠRec ruit ment Ġof Ġparticipants Ġwith Ġcognitive Ġimpairment Ġhas Ġhistorically Ġbeen Ġa Ġbottleneck Ġin Ġclinical Ġtrials ĠB IB REF 1 Ġ, Ġmaking ĠAD Ġdatasets Ġrelatively Ġsmall . ĠAdditionally , Ġthough Ġcognitive Ġassessments Ġtest Ġdomains Ġof Ġcognition Ġthrough Ġmultiple Ġtasks , Ġmost Ġavailable Ġdatasets Ġof Ġpathological Ġspeech Ġare Ġrestricted Ġto Ġparticipants Ġperforming Ġa Ġsingle Ġtask . ĠPicture Ġdescription Ġusing Ġan Ġimage Ġto Ġelicit Ġnarrative Ġdiscourse Ġsamples Ġis Ġone Ġsuch Ġtask Ġthat Ġhas Ġproved Ġto Ġbe Ġsuccessful Ġin Ġdetecting ĠAD ĠB IB REF 2 Ġ. ĠHowever , Ġit Ġis Ġimportant Ġto Ġdevelop ĠML Ġmodels Ġof Ġhigh Ġperformance Ġthat Ġwould Ġproduce Ġresults Ġgeneral izable Ġacross Ġdifferent Ġtasks . </s> Several Ġstudies Ġhave Ġused Ġnatural Ġlanguage Ġprocessing Ġand Ġmachine Ġlearning Ġto Ġdistinguish Ġbetween Ġhealthy Ġand Ġcognitive ly Ġimpaired Ġspeech Ġof Ġparticipants Ġdescribing Ġa Ġpicture . ĠFraser Ġet Ġal . ĠB IB REF 3 Ġused Ġlinguistic Ġand Ġacoustic Ġfeatures Ġto Ġclassify Ġhealthy Ġand Ġpathological Ġspeech Ġtranscripts Ġwith Ġan Ġaccuracy Ġof ĠIN LINE FORM 0 Ġ. ĠSimilarly , ĠKar le kar Ġet Ġal . ĠB IB REF 4 Ġclassified Ġutter ances Ġof Ġspeakers Ġas ĠAD Ġor Ġhealthy Ġ( HC ) Ġwith Ġan Ġaccuracy Ġof ĠIN LINE FORM 1 Ġusing Ġan Ġenlarged , Ġutter ance - level Ġview Ġof Ġtranscripts Ġof Ġpicture Ġdescriptions . ĠIn Ġline Ġwith Ġprevious Ġresearch , Ġwe Ġuse Ġlinguistic Ġand Ġacoustic Ġfeatures Ġof Ġspeech Ġas Ġinput Ġto Ġour ĠML Ġmodel . ĠFurthermore , Ġwe Ġextend Ġthe Ġmodel Ġto Ġusing Ġdata Ġfrom Ġseveral Ġdifferent Ġtasks . </s> N oor ian Ġet Ġal . ĠB IB REF 5 Ġdemonstrated Ġthat Ġusing Ġwithin - task Ġdata Ġof Ġhealthy Ġparticipants Ġdescribing Ġa Ġpicture Ġimproved ĠAD Ġdetection Ġperformance Ġby Ġup Ġto Ġ13 %. ĠIn Ġthis Ġpaper , Ġwe Ġevaluate Ġif Ġmodel Ġperformance Ġimproves Ġwith Ġthe Ġaddition Ġof Ġdata Ġfrom Ġhealthy Ġparticipants , Ġwith Ġvarying Ġages , Ġperforming Ġeither Ġthe Ġsame Ġor Ġdifferent Ġtasks . ĠWe Ġfind Ġthat Ġmodels Ġtrained Ġon Ġdatasets Ġof Ġpicture Ġdescription Ġtasks Ġaugmented Ġwith Ġconvers ational Ġspeech Ġof Ġhealthy Ġspeakers Ġlearn Ġdecision Ġboundaries Ġthat Ġare Ġmore Ġgeneral izable Ġacross Ġactivities Ġwith Ġlower Ġout - of - task Ġerrors . ĠWe Ġobserve Ġa Ġ9 % Ġincrease Ġin ĠAD Ġdetection Ġperformance Ġwhen Ġnormative Ġdata Ġfrom Ġdifferent Ġtasks Ġare Ġutilized . ĠWe Ġalso Ġanalyze Ġif Ġeach Ġtask Ġprovides Ġdomain - specific Ġinduct ive Ġbias Ġfor Ġother Ġtasks Ġto Ġobtain Ġa Ġmodel Ġsetting Ġcapable Ġof Ġdetecting ĠAD Ġfrom Ġany Ġsample Ġof Ġspeech Ġusing Ġhigh - pre cision Ġmodel - agn ostic Ġexplanations Ġproposed Ġby ĠRib e iro Ġet Ġal . ĠB IB REF 6 Ġand Ġcomputation Ġof Ġvarious Ġerror Ġmetrics Ġrelated Ġto Ġclassification . </s> Data </s> All Ġdatasets Ġshown Ġin ĠTab . ĠSEC REF 2 Ġwere Ġtransc ribed Ġmanually Ġby Ġtrained Ġtranscription ists , Ġemploying Ġthe Ġsame Ġlist Ġof Ġannotations Ġand Ġprotocols , Ġwith Ġthe Ġsame Ġset Ġof Ġfeatures Ġextracted Ġfrom Ġthe Ġtranscripts Ġ( see ĠSec . ĠSEC REF 3 Ġ). ĠH APD Ġand ĠHA FP Ġare Ġjointly Ġreferred Ġto Ġas ĠHA . </s> Methods </s> Feature ĠExt raction : ĠWe Ġextract Ġ297 Ġlinguistic Ġfeatures Ġfrom Ġthe Ġtranscripts Ġand Ġ183 Ġacoustic Ġfeatures Ġfrom Ġthe Ġassociated Ġaudio Ġfiles , Ġall Ġtask - independent . ĠL ingu istic Ġfeatures Ġencompass Ġsynt actic Ġfeatures Ġ( e . g . Ġsynt actic Ġcomplexity ĠB IB REF 9 Ġ), Ġlex ical Ġfeatures Ġ( e . g . Ġoccurrence Ġof Ġproduction Ġrules ). ĠAc oustic Ġfeatures Ġinclude ĠMel - frequency ĠC ep st ral ĠCo efficients Ġ( MF CC s ) Ġ& Ġpause - related Ġfeatures Ġ( e . g ., Ġmean Ġpause Ġduration ). ĠWe Ġalso Ġuse Ġsentiment Ġlex ical Ġnorms ĠB IB REF 10 Ġ, Ġlocal , Ġand Ġglobal Ġco herence Ġfeatures ĠB IB REF 11 Ġ. </s> Feature ĠPred icates Ġas ĠAnch ors Ġfor ĠPrediction : ĠGiven Ġa Ġblack Ġbox Ġclass ifier ĠIN LINE FORM 0 Ġwith Ġinterpret able Ġinput Ġrepresentation , ĠRib e iro Ġet Ġal . ĠB IB REF 6 Ġdefine Ġanchors ĠIN LINE FORM 1 Ġas Ġa Ġset Ġof Ġinput Ġrules Ġsuch Ġthat Ġwhen Ġconditions Ġin Ġthe Ġrule Ġare Ġmet , Ġhumans Ġcan Ġconfidently Ġpredict Ġthe Ġbehavior Ġof Ġa Ġmodel Ġwith Ġhigh Ġprecision . ĠSince Ġthe Ġinputs Ġto Ġthe Ġclass ifier Ġare Ġengineered Ġfeatures Ġwith Ġfinite Ġranges , we Ġcan Ġobtain Ġsufficient Ġconditions Ġfor Ġthe Ġprediction ĠIN LINE FORM 2 Ġin Ġterms Ġof Ġinterpret able Ġfeature Ġthresholds Ġfor Ġan Ġunseen Ġinstance ĠIN LINE FORM 3 Ġ. ĠAnch ors Ġare Ġfound Ġby Ġmaximizing Ġthe Ġmetric Ġof Ġcoverage , Ġdefined Ġas Ġthe Ġprobability Ġof Ġanchors Ġholding Ġtrue Ġto Ġsamples Ġin Ġthe Ġdata Ġdistribution ĠIN LINE FORM 4 Ġ, Ġin ĠB IB REF 6 Ġ. ĠHence , ĠIN LINE FORM 5 Ġis Ġmaxim ized , Ġwhere ĠIN LINE FORM 6 Ġ. </s> We Ġshow Ġin ĠSec . ĠSEC REF 13 Ġthat Ġanchors Ġidentified Ġfrom Ġa Ġmodel Ġtrained Ġon Ġmultiple Ġtasks Ġhave Ġmore Ġcoverage Ġover Ġthe Ġdata Ġdistribution Ġthan Ġthose Ġobtained Ġfrom Ġa Ġmodel Ġtrained Ġon Ġa Ġsingle Ġtask . ĠSuch Ġa Ġscenario Ġis Ġpossible Ġwhen Ġtask - ind epend ant , Ġclinically Ġrelevant Ġspeech Ġfeatures Ġare Ġselected Ġas Ġanchors Ġ( e . g ., Ġfraction Ġof Ġfilled Ġpauses Ġin Ġspeech ĠB IB REF 12 Ġ, Ġacoustic Ġfeatures ĠB IB REF 13 Ġetc . Ġ). ĠAdditionally , Ġsuch Ġselected Ġanchors Ġmust Ġalso Ġbe Ġassociated Ġwith Ġthresholds Ġapplicable Ġacross Ġmultiple Ġtypes Ġof Ġspeech . </s> Exper iments </s> B inary Ġclassification Ġof Ġeach Ġspeech Ġtranscript Ġas ĠAD Ġor ĠHC Ġis Ġperformed . ĠWe Ġdo Ġ5 - fold Ġcross - valid ation , Ġstrat ified Ġby Ġsubject Ġso Ġthat Ġeach Ġsubject 's Ġsamples Ġdo Ġnot Ġoccur Ġin Ġboth Ġtraining Ġand Ġtesting Ġsets Ġin Ġeach Ġfold . ĠThe Ġminority Ġclass Ġis Ġovers am pled Ġin Ġthe Ġtraining Ġset Ġusing ĠSM OTE ĠB IB REF 14 Ġto Ġdeal Ġwith Ġthe Ġclass Ġimbalance . ĠWe Ġconsider Ġa ĠRandom ĠForest Ġ( 100 Ġtrees ), ĠNa Ã¯ve ĠBay es Ġ( with Ġequal Ġpri ors ), ĠS VM Ġ( with ĠRB F Ġkernel ), Ġand Ġa Ġ2 - layer Ġneural Ġnetwork Ġ( 10 Ġunits , ĠAdam Ġoptim izer , Ġ500 Ġepoch s ) ĠB IB REF 15 Ġ. ĠAdditionally , Ġwe Ġaugment Ġthe ĠDB Ġdata Ġwith Ġhealthy Ġsamples Ġfrom ĠFP Ġwith Ġvaried Ġages . </s> Results Ġand ĠDiscussion </s> Visual ization Ġof ĠClass ĠBound aries </s> Since Ġdata Ġof Ġdifferent Ġtasks Ġhave Ġdifferent Ġnoise Ġpatterns , Ġthe Ġprobability Ġof Ġover fitting Ġto Ġnoise Ġis Ġreduced Ġwith Ġsamples Ġfrom Ġdifferent Ġtasks . ĠThis Ġcan Ġalso Ġbe Ġvisual ized Ġas Ġdecision Ġboundaries Ġof Ġmodels Ġtrained Ġon Ġvarious Ġdataset Ġcombinations . ĠFor ĠFig . ĠSEC REF 2 Ġ, Ġwe Ġembed Ġthe Ġ480 - dimensional Ġfeature Ġvector Ġinto Ġ2 Ġdimensions Ġusing ĠLoc ally ĠLinear ĠEmb edd ings ĠB IB REF 16 Ġtrained Ġon ĠDB . </s> In Ġdatasets Ġconsisting Ġof Ġpicture Ġdescriptions Ġand Ġconvers ational Ġspeech Ġ( DB Ġ+ ĠFP ), Ġthe Ġfeature Ġranges Ġincrease Ġas Ġcompared Ġto Ġpicture Ġdescription Ġtasks , Ġso Ġit Ġis Ġexpected Ġthat Ġa Ġclass ifier Ġtrained Ġon Ġstructured Ġtasks Ġonly Ġ( DB Ġ+ ĠHA FP ) Ġwould Ġincorrectly Ġclassify Ġhealthy Ġsamples Ġin Ġthe Ġfourth Ġquad rant Ġ( error Ġrates Ġfor Ġtasks Ġnot Ġin Ġdataset Ġis Ġ17 . 8 %). ĠHowever , Ġdecision Ġboundaries Ġfor Ġmodels Ġtrained Ġon Ġa Ġmix Ġof Ġstructured Ġtasks Ġand Ġun struct ured Ġconvers ational Ġspeech Ġseem Ġto Ġbe Ġmore Ġgeneral izable Ġacross Ġtasks . ĠE . g ., Ġdecision Ġboundaries Ġobtained Ġfrom ĠDB Ġ+ ĠFP Ġcould Ġapply Ġto Ġmost Ġdat ap oint s Ġin ĠHA FP Ġ( out Ġof Ġtask Ġerror Ġrate Ġis Ġ3 . 6 %). ĠClin ically , Ġsome Ġof Ġthe Ġfeatures Ġused Ġsuch Ġas Ġthe Ġpatterns Ġin Ġusage Ġof Ġfunction Ġwords Ġlike Ġpronouns Ġhave Ġshown Ġto Ġreflect Ġstress - related Ġchanges Ġin Ġgene Ġexpression , Ġpossibly Ġcaused Ġdue Ġto Ġdementia ĠB IB REF 17 Ġwhich Ġwould Ġnot Ġdepend Ġon Ġthe Ġtask Ġtype Ġand Ġcould Ġexplain Ġsuch Ġa Ġcommon Ġunderlying Ġstructure Ġto Ġfeatures . </s> Class ification ĠPerformance </s> Results Ġof Ġbinary Ġclassification Ġwith Ġdifferent Ġdataset Ġcombinations Ġ( i . e ., Ġthe Ġproportion Ġof Ġeach Ġdataset Ġused ) Ġare Ġin ĠTab . ĠSEC REF 7 Ġ. ĠThe Ġhighest ĠF 1 Ġscore Ġon ĠDB Ġis ĠIN LINE FORM 0 Ġwith ĠS VM Ġas Ġobtained Ġby ĠNo orian Ġet Ġal . ĠB IB REF 5 Ġ, Ġenabling Ġsimilar Ġcomparisons . </s> We Ġsee Ġthe Ġsame Ġtrend Ġof Ġincreasing Ġmodel Ġperformance Ġwith Ġnormative Ġdata Ġfrom Ġthe Ġpicture Ġdescription Ġtask , Ġas Ġshown Ġby ĠNo orian Ġet Ġal . ĠB IB REF 5 Ġ. ĠWe Ġobserve Ġthat Ġthis Ġincrease Ġis Ġindependent Ġof Ġthe Ġnature Ġof Ġthe Ġtask Ġperformed ĠâĢĵ Ġnormative Ġpicture Ġdescription Ġtask Ġdata Ġof Ġsimilar Ġsize Ġas Ġin ĠB IB REF 5 Ġand Ġthe Ġsame Ġamount Ġof Ġnormative Ġdata Ġfrom Ġdifferent Ġstructured Ġtasks Ġof Ġflu ency Ġtests Ġand Ġparagraph Ġreading Ġprove Ġto Ġbe Ġhelpful , Ġbringing Ġabout Ġa Ġsimilar Ġincrease Ġin Ġscores Ġ(+ 2 %, Ġ+ 5 % Ġabsolute ĠF 1 Ġmicro Ġand Ġmacro ). ĠInterestingly , Ġperformance Ġof Ġdetecting Ġthe Ġmajority Ġ( healthy ) Ġclass Ġ( ref lected Ġin ĠF 1 Ġmicro ) Ġas Ġwell Ġas Ġthe Ġminority Ġ( AD ) Ġclass Ġ( ref lected Ġin ĠF 1 Ġmacro ) Ġincreases Ġwith Ġadditional Ġdata . </s> Aug ment ing ĠDB Ġwith Ġsame Ġamount Ġof Ġsamples Ġfrom Ġstructured Ġtasks Ġ( HA ) Ġand Ġfrom Ġconvers ational Ġspeech Ġ( FP ) Ġbrings Ġabout Ġsimilar Ġperformance . ĠDou bling Ġthe Ġinitial Ġamount Ġof Ġcontrol Ġdata Ġwith Ġdata Ġfrom Ġa Ġdifferent Ġstructured Ġtask Ġ( HA , ĠHA FP ) Ġresults Ġin Ġan Ġincrease Ġof Ġup Ġto Ġ9 % Ġin ĠF 1 Ġscores . </s> Imp act Ġof ĠAge </s> We Ġaugment ĠDB Ġwith Ġhealthy Ġsamples Ġfrom ĠFP Ġwith Ġvarying Ġages Ġ( Tab . ĠSEC REF 11 Ġ), Ġconsidering Ġ50 Ġsamples Ġfor Ġeach Ġ15 Ġyear Ġduration Ġstarting Ġfrom Ġage Ġ30 . ĠAdding Ġthe Ġsame Ġnumber Ġof Ġsamples Ġfrom Ġbins Ġof Ġage Ġgreater Ġthan Ġ60 Ġleads Ġto Ġgreater Ġincrease Ġin Ġperformance . ĠThis Ġcould Ġbe Ġbecause Ġthe Ġaverage Ġage Ġof Ġparticipants Ġin Ġthe Ġdatasets Ġ( DB , ĠHA Ġetc .) Ġwe Ġuse Ġare Ġgreater Ġthan Ġ60 . ĠNote Ġthat Ġdespite Ġsuch Ġa Ġtrend , Ġaddition Ġof Ġhealthy Ġdata Ġproduces Ġfair Ġclass ifiers Ġwith Ġrespect Ġto Ġsamples Ġwith Ġage ĠIN LINE FORM 0 Ġ60 Ġand Ġthose Ġwith Ġage ĠIN LINE FORM 1 Ġ60 Ġ( balanced ĠF 1 Ġscores Ġof Ġ75 . 6 % Ġand Ġ76 . 1 % Ġrespectively ; Ġfurther Ġdetails Ġin ĠApp . ĠSEC REF 43 Ġ.) </s> Ind uctive ĠB ias Ġof ĠT asks </s> Each Ġtask Ġperformed Ġin Ġthe Ġdatasets Ġis Ġdesigned Ġto Ġassess Ġdifferent Ġcognitive Ġfunctions , Ġe . g . Ġflu ency Ġtask Ġis Ġused Ġto Ġevaluate Ġthe Ġability Ġto Ġorganize Ġand Ġplan ĠB IB REF 18 Ġand Ġpicture Ġdescription Ġtask ĠâĢĵ Ġfor Ġdetecting Ġdiscourse - related Ġimpair ments ĠB IB REF 19 Ġ. ĠAs Ġa Ġresult , Ġit Ġis Ġexpected Ġthat Ġthe Ġnature Ġof Ġdecision Ġfunctions Ġand Ġfeature Ġpred icates Ġlearned Ġon Ġdata Ġof Ġeach Ġof Ġthese Ġtasks Ġwould Ġbe Ġdifferent . ĠPerformance Ġof ĠAD Ġidentification Ġwith Ġaddition Ġof Ġnormative Ġdata Ġfrom Ġmultiple Ġtasks Ġ( Tab . ĠSEC REF 7 Ġ), Ġdespite Ġthe Ġpossibly Ġdifferent Ġnature Ġof Ġdecision Ġfunctions , Ġsuggests Ġthat Ġtraining Ġthe Ġmodel Ġwith Ġsamples Ġfrom Ġeach Ġtask Ġprovides Ġdomain - specific Ġinduct ive Ġbias Ġfor Ġother Ġtasks . ĠWe Ġstudy Ġpossible Ġunderlying Ġmechanisms Ġresponsible Ġfor Ġthis , Ġsuggested Ġby ĠCar uana Ġet Ġal . ĠB IB REF 20 Ġand ĠRud er Ġet Ġal . ĠB IB REF 21 Ġ. </s> Att ention - f ocusing Ġon ĠRe levant ĠFeatures : ĠRud er Ġet Ġal . ĠB IB REF 21 Ġclaim Ġthat Ġin Ġa Ġsmall , Ġhigh - dimensional Ġdataset , Ġinformation Ġregarding Ġrelevance Ġor Ġirre lev ance Ġof Ġparticular Ġfeatures Ġis Ġdifficult Ġto Ġcapture . ĠHowever , Ġdata Ġrelated Ġto Ġmultiple Ġtasks Ġcan Ġhelp Ġidentify Ġfeatures Ġrelevant Ġacross Ġdifferent Ġactivities . ĠWe Ġcan Ġuse Ġanchor Ġvariables ĠB IB REF 6 Ġto Ġshow Ġthis Ġeffect . ĠThe Ġcoverage Ġof Ġfeatures Ġanch oring Ġthe Ġprediction Ġof Ġan Ġinstance Ġindicates Ġthe Ġapplic ability Ġof Ġthe Ġfeature Ġpredicate Ġto Ġthe Ġrest Ġof Ġthe Ġdata Ġdistribution Ġand Ġhence Ġthe Ġimportance Ġof Ġthe Ġfeature Ġacross Ġthe Ġdata Ġdistribution . ĠThe Ġcoverage Ġof Ġthe Ġanchors Ġselected Ġfor Ġa Ġtest Ġset Ġwhich Ġis Ġ10 % Ġ( 50 Ġsamples ) Ġof ĠDB Ġchanges Ġby Ġ40 . 8 % Ġ( from Ġ0 . 05 Ġto Ġ0 . 07 ) Ġon Ġthe Ġaddition Ġof Ġthe ĠHA , Ġwhich Ġindicates Ġthat Ġthere Ġis Ġan Ġattention Ġfocusing Ġeffect . </s> Represent ation Ġbias : ĠAs Ġshown Ġby ĠSch ulz Ġet Ġal . ĠB IB REF 22 Ġ, Ġmodels Ġtrained Ġon Ġdata Ġfrom Ġmultiple Ġtasks Ġperform Ġbetter Ġthan Ġwith Ġsingle - task Ġinformation Ġwhen Ġlittle Ġtraining Ġdata Ġis Ġavailable Ġfor Ġthe Ġmain Ġtask . ĠThe Ġnon - linear Ġtrend Ġof Ġincrease Ġin Ġmodel Ġperformance Ġwith Ġthe Ġaddition Ġof Ġdifferent Ġamounts Ġof Ġdata Ġis Ġshown Ġin ĠApp . ĠSEC REF 41 Ġ. ĠThe ĠF 1 Ġmicro Ġscore Ġof Ġthe Ġbest Ġperforming Ġmodel Ġtrained Ġon ĠDB Ġ+ ĠHA Ġis Ġ82 . 28 % Ġfor Ġpicture Ġdescription Ġtasks , Ġ95 . 4 % Ġfor Ġparagraph Ġreading Ġand Ġ97 . 01 % Ġfor Ġflu ency Ġtasks . ĠThis Ġshows Ġgreater Ġthan Ġtrivial Ġperformance Ġfor Ġeach Ġtask Ġand Ġimprovement Ġin Ġperformance Ġfor Ġpicture Ġdescription Ġtask Ġfrom Ġtraining Ġa Ġmodel Ġpurely Ġon ĠDB . ĠSuch Ġan Ġeffect Ġhelps Ġthe Ġmodel Ġachieve Ġnon - t riv ial Ġperformance Ġon ĠAD Ġdetection Ġfor Ġnovel Ġtasks Ġmeasuring Ġmultiple Ġdomains Ġof Ġcognition , Ġgiven Ġa Ġsufficiently Ġlarge Ġnumber Ġof Ġtraining Ġtasks Ġaccording Ġto Ġalgorithms Ġprovided Ġby ĠBaxter Ġet Ġal . ĠB IB REF 23 Ġ. ĠHence , Ġtraining Ġmodels Ġon Ġmany Ġspeech - based Ġtasks Ġcould Ġhelp Ġdevelop Ġan Ġalgorithm Ġcapable Ġof Ġdetecting ĠAD Ġfrom Ġany Ġsample Ġof Ġspontaneous Ġspeech . </s> O ng oing Ġwork Ġis Ġon Ġdetailed Ġanalysis Ġof Ġnature Ġand Ġpol arity Ġof Ġfeature Ġtrends Ġacross Ġvarious Ġspeech Ġtasks . ĠFuture Ġwork Ġwill Ġfocus Ġon Ġlearning Ġinterpret able Ġlatent Ġrepresentations Ġbased Ġon Ġthe Ġobservations Ġmade , Ġcapable Ġof Ġgood Ġpredictive Ġperformance Ġacross Ġa Ġmultitude Ġof Ġtasks . </s> Detailed ĠDescription Ġof ĠDat as ets </s> D ement ia Bank Ġ( DB ): ĠThe ĠD ement ia Bank Ġdataset Ġis Ġthe Ġlargest Ġavailable Ġpublic Ġdataset Ġof Ġspeech Ġfor Ġassessing Ġcognitive Ġimpair ments . ĠIt Ġconsists Ġof Ġnarrative Ġpicture Ġdescriptions Ġfrom Ġparticipants Ġaged Ġbetween Ġ45 Ġto Ġ90 ĠB IB REF 24 Ġ. ĠIn Ġeach Ġsample , Ġa Ġparticipant Ġdescribes Ġthe Ġpicture Ġthat Ġthey Ġare Ġshown . ĠOut Ġof Ġthe Ġ210 Ġparticipants Ġin Ġthe Ġstudy , Ġ117 Ġwere Ġdiagnosed Ġwith ĠAD Ġ( ĠIN LINE FORM 0 Ġsamples Ġof Ġspeech ) Ġand Ġ93 Ġwere Ġhealthy Ġ( HC ; ĠIN LINE FORM 1 Ġsamples ) Ġwith Ġmany Ġsubjects Ġrepeating Ġthe Ġtask Ġwith Ġan Ġinterval Ġof Ġa Ġyear . ĠDem ographics Ġof Ġage , Ġsex , Ġand Ġyears Ġof Ġeducation Ġare Ġprovided Ġin Ġthe Ġdataset . </s> Health y ĠAging Ġ( HA ) Ġ: ĠThe ĠHealthy ĠAging Ġdataset Ġconsists Ġof Ġspeech Ġsamples Ġof Ġcognitive ly Ġhealthy Ġparticipants Ġ( ĠIN LINE FORM 0 Ġ) Ġolder Ġthan Ġ50 Ġyears . ĠEach Ġparticipant Ġperforms Ġthree Ġstructured Ġtasks ĠâĢĵ Ġpicture Ġdescription Ġ( H APD ), Ġverbal Ġflu ency Ġtest , Ġand Ġa Ġparagraph Ġreading Ġtask . ĠFlu ency Ġand Ġparagraph Ġtasks Ġare Ġjointly Ġreferred Ġto Ġas ĠHA FP . ĠThe Ġaverage Ġnumber Ġof Ġsamples Ġper Ġparticipant Ġis Ġ14 . 46 . ĠThe Ġdataset Ġconstitutes Ġ8 . 5 Ġhours Ġof Ġtotal Ġaudio . </s> F amous ĠPeople Ġ( FP ): ĠThe ĠFamous ĠPeople Ġdataset ĠB IB REF 8 Ġconsists Ġof Ġpublicly Ġavailable Ġspontaneous Ġspeech Ġsamples Ġfrom Ġ9 Ġfamous Ġindividuals Ġ( e . g ., ĠWoody ĠAllen Ġ& ĠClint ĠEast wood ) Ġover Ġthe Ġperiod Ġfrom Ġ1956 Ġto Ġ2017 , Ġspanning Ġperiods Ġfrom Ġearly Ġadulthood Ġto Ġolder Ġage , Ġwith Ġan Ġaverage Ġof Ġ25 Ġsamples Ġper Ġperson . ĠWe Ġuse Ġspeech Ġsamples Ġof Ġthese Ġsubjects Ġwho Ġare Ġconsidered Ġto Ġbe Ġhealthy Ġ( ĠIN LINE FORM 0 Ġ), Ġgiven Ġan Ġabsence Ġof Ġany Ġreported Ġdiagnosis Ġor Ġsubjective Ġmemory Ġcomplaints . ĠThis Ġhealthy Ġcontrol Ġ( HC ) Ġgroup Ġcovers Ġa Ġvariety Ġof Ġspeaker Ġages , Ġfrom Ġ30 Ġto Ġ88 Ġ( ĠIN LINE FORM 1 Ġ, ĠIN LINE FORM 2 Ġ). </s> Features </s> A Ġlist Ġof Ġ480 Ġfeatures Ġbelonging Ġto Ġthree Ġgroups Ġ- Ġacoustic , Ġsemantic / Ġsynt actic Ġand Ġlex ical . ĠThese Ġfeatures Ġinclude Ġconstituency - p ars ing Ġbased Ġfeatures , Ġsynt actic Ġcomplexity Ġfeatures Ġextracted Ġusing ĠLu ĠSynt actic ĠComplex ity Ġanaly zer ĠB IB REF 9 Ġ, ĠMF CC Ġmeans , Ġvar iances Ġand Ġother Ġhigher Ġorder Ġmoments . ĠFew Ġof Ġthese Ġfeatures Ġare Ġlisted Ġbelow Ġ: </s> Ph on ation Ġrate Ġ: ĠPercentage Ġof Ġrecording Ġthat Ġis Ġvoiced . </s> Me an Ġpause Ġduration Ġ: ĠMean Ġduration Ġof Ġpauses Ġin Ġseconds . </s> Pause Ġword Ġratio Ġ: ĠRatio Ġof Ġsilent Ġsegments Ġto Ġvoiced Ġsegments . </s> Short Ġpause Ġcount Ġnormalized Ġ: ĠNormal ized Ġnumber Ġof Ġpauses Ġless Ġthan Ġ1 Ġsecond . </s> Medium Ġpause Ġcount Ġnormalized Ġ: ĠNormal ized Ġnumber Ġof Ġpauses Ġbetween Ġ1 Ġsecond Ġand Ġ2 Ġseconds Ġin Ġlength . </s> Z CR Ġk urt osis Ġ: ĠKurt osis Ġof ĠZero ĠCrossing ĠRate Ġ( Z CR ) Ġof Ġall Ġvoiced Ġsegments Ġacross Ġframes . </s> MF CC Ġmeans Ġ: ĠMean Ġof Ġvelocity Ġof ĠMF CC Ġcoefficient Ġover Ġall Ġframes Ġ( this Ġis Ġcalculated Ġfor Ġmultiple Ġcoefficients ). </s> MF CC Ġk urt osis : ĠKurt osis Ġof Ġmean Ġfeatures . </s> MF CC Ġvariance : ĠVari ance Ġof Ġacceleration Ġof Ġframe Ġenergy Ġover Ġall Ġframes . </s> Moving - average Ġtype - token Ġratio Ġ( MAT TR ): ĠMoving Ġaverage ĠT TR Ġ( type - token Ġratio ) Ġover Ġa Ġwindow Ġof Ġ10 Ġtokens . </s> Cos ine Ġcutoff Ġ: ĠF raction Ġof Ġpairs Ġof Ġutter ances Ġwith Ġcos ine Ġdistance ĠIN LINE FORM 0 Ġ0 . 001 . </s> Pa uses Ġof Ġtype Ġ` uh ' Ġ: ĠThe Ġnumber Ġof Ġ` uh ' Ġfill ers Ġover Ġall Ġtokens . </s> Numbers Ġof Ġinter ject ions / n umer als Ġ: ĠThe Ġnumber Ġof Ġinter ject ions / n umer als Ġused Ġover Ġall Ġtokens . </s> N oun Ġratio : ĠRatio Ġof Ġnumber Ġof Ġnoun s Ġto Ġnumber Ġof Ġnoun s Ġ+ Ġverbs . </s> Tem poral Ġcohesion Ġfeature Ġ: ĠAverage Ġnumber Ġof Ġswitches Ġin Ġtense . </s> Spe ech Ġgraph Ġfeatures Ġ: ĠFeatures Ġextracted Ġfrom Ġgraph Ġof Ġspoken Ġwords Ġin Ġa Ġsample Ġincluding Ġaverage Ġtotal Ġdegree , Ġnumber Ġof Ġedges , Ġaverage Ġshortest Ġpath , Ġgraph Ġdiameter Ġ( und irect ed ) Ġand Ġgraph Ġdensity . </s> F illed Ġpauses Ġ: ĠNumber Ġof Ġnon - sil ent Ġpauses . </s> N oun Ġfrequency Ġ: ĠAverage Ġfrequency Ġnorm Ġfor Ġall Ġnoun s . </s> N oun Ġimage ability : ĠAverage Ġimage ability Ġnorm Ġfor Ġall Ġnoun s . </s> Features Ġfrom Ġparse - tree Ġ: ĠNumber Ġof Ġtimes Ġproduction Ġrules Ġsuch Ġas Ġnumber Ġof Ġnoun Ġphrases Ġto Ġdetermin ers Ġoccurrences , Ġoccur Ġover Ġthe Ġtotal Ġnumber Ġof Ġproductions Ġin Ġthe Ġtranscript 's Ġparse Ġtree . </s> Sy nt actic Ġcomplexity Ġfeatures : ĠRatio Ġof Ġclauses Ġto ĠT - units , ĠRatio Ġof Ġclauses Ġto Ġsentences Ġetc . ĠB IB REF 9 </s> Hyper - param eters : </s> Ga ussian ĠNa ive ĠBay es Ġwith Ġbalanced Ġpri ors Ġis Ġused . </s> The Ġrandom Ġforest Ġclass ifier Ġfits Ġ100 Ġdecision Ġtrees Ġwith Ġother Ġdefault Ġparameters Ġin ĠB IB REF 15 Ġ. </s> S VM Ġis Ġtrained Ġwith Ġradial Ġbasis Ġfunction Ġkernel , Ġregular ization Ġparameter ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġ. </s> The ĠN N Ġconsists Ġof Ġone Ġhidden Ġlayer Ġof Ġ10 Ġunits . ĠThe Ġtan h Ġactivation Ġfunction Ġis Ġused Ġat Ġeach Ġhidden Ġlayer . ĠThe Ġnetwork Ġis Ġtrained Ġusing ĠAdam Ġfor Ġ100 Ġepoch s Ġwith Ġother Ġdefault Ġparameters Ġin ĠB IB REF 15 Ġ. </s> Effect Ġof Ġdata Ġfrom Ġdifferent Ġtasks : </s> The Ġeffect Ġof Ġaugment ing ĠDB Ġwith Ġdata Ġfrom Ġa Ġdifferent Ġstructured Ġtask Ġ( HA FP ) Ġis Ġshown Ġin ĠSEC REF 41 Ġ. </s> F 1 Ġscores Ġ( micro Ġand Ġmacro ) Ġincrease Ġnon - lin early Ġwith Ġthe Ġaddition Ġof Ġdata . </s> Fair ness Ġwith ĠRespect Ġto ĠAge : </s> We Ġevaluate Ġfairness Ġof Ġclassification Ġwith Ġrespect Ġto Ġtwo Ġgroups Ġ- Ġsamples Ġwith Ġage ĠIN LINE FORM 0 Ġ60 Ġand Ġthose Ġwith Ġage ĠIN LINE FORM 1 Ġ60 . ĠA Ġfair Ġclass ifier Ġwould Ġproduce Ġcomparable Ġclassification Ġscores Ġfor Ġboth Ġgroups . ĠFor Ġthe Ġbest Ġperforming Ġclass ifier Ġon ĠDB , Ġthe ĠF 1 Ġ( micro ) Ġscore Ġfor Ġsamples Ġwith Ġage ĠIN LINE FORM 2 Ġ60 Ġis Ġ85 . 9 % Ġand Ġwith Ġage ĠIN LINE FORM 3 Ġ60 Ġis Ġ76 . 4 %. ĠWith Ġthe Ġaddition Ġof ĠHA , Ġthe ĠF 1 Ġ( micro ) Ġscore Ġfor Ġsamples Ġwith Ġage ĠIN LINE FORM 4 Ġ60 Ġand Ġwith Ġage ĠIN LINE FORM 5 Ġis Ġmore Ġbalanced Ġ( 75 . 6 %, Ġ76 . 1 % Ġrespectively ) Ġfor Ġthe Ġsame Ġset Ġof Ġdata Ġpoints Ġfrom ĠDB . ĠNote Ġthat Ġthe Ġaverage Ġage Ġin Ġboth Ġdatasets Ġare Ġsimilar Ġ( ĠIN LINE FORM 6 Ġ).