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1912.03627
Introduction </s> Now adays Ġdeep Ġlearning Ġtechniques Ġoutper form Ġthe Ġother Ġconventional Ġmethods Ġin Ġmost Ġof Ġthe Ġspeech - related Ġtasks . ĠTraining Ġrobust Ġdeep Ġneural Ġnetworks Ġfor Ġeach Ġtask Ġdepends Ġon Ġthe Ġavailability Ġof Ġpowerful Ġprocessing ĠGPUs , Ġas Ġwell Ġas Ġstandard Ġand Ġlarge Ġscale Ġdatasets . ĠIn Ġtext - independent Ġspeaker Ġverification , Ġlarge - scale Ġdatasets Ġare Ġavailable , Ġthanks Ġto Ġthe ĠN IST ĠS RE Ġevaluations Ġand Ġother Ġdata Ġcollection Ġprojects Ġsuch Ġas ĠVox Cele b ĠB IB REF 0 . </s> In Ġtext - dependent Ġspeaker Ġrecognition , Ġexperiments Ġwith Ġend - to - end Ġarchitectures Ġconducted Ġon Ġlarge Ġproprietary Ġdatabases Ġhave Ġdemonstrated Ġtheir Ġsuperiority Ġover Ġtraditional Ġapproaches ĠB IB REF 1 . ĠYet , Ġcontrary Ġto Ġtext - independent Ġspeaker Ġrecognition , Ġtext - dependent Ġspeaker Ġrecognition Ġlacks Ġlarge - scale Ġpublicly Ġavailable Ġdatabases . ĠThe Ġtwo Ġmost Ġwell - known Ġdatasets Ġare Ġprobably ĠR SR 2015 ĠB IB REF 2 Ġand ĠRed D ots ĠB IB REF 3 . ĠThe Ġformer Ġcontains Ġspeech Ġdata Ġcollected Ġfrom Ġ300 Ġindividuals Ġin Ġa Ġcontrolled Ġmanner , Ġwhile Ġthe Ġlatter Ġis Ġused Ġprimarily Ġfor Ġevaluation Ġrather Ġthan Ġtraining , Ġdue Ġto Ġits Ġsmall Ġnumber Ġof Ġspeakers Ġ( only Ġ64 ). ĠMot ivated Ġby Ġthis Ġlack Ġof Ġlarge - scale Ġdataset Ġfor Ġtext - dependent Ġspeaker Ġverification , Ġwe Ġchose Ġto Ġproceed Ġwith Ġthe Ġcollection Ġof Ġthe ĠDeep Mine Ġdataset , Ġwhich Ġwe Ġexpect Ġto Ġbecome Ġa Ġstandard Ġbenchmark Ġfor Ġthe Ġtask . </s> Apart Ġfrom Ġspeaker Ġrecognition , Ġlarge Ġamounts Ġof Ġtraining Ġdata Ġare Ġrequired Ġalso Ġfor Ġtraining Ġautomatic Ġspeech Ġrecognition Ġ( AS R ) Ġsystems . ĠSuch Ġdatasets Ġshould Ġnot Ġonly Ġbe Ġlarge Ġin Ġsize , Ġthey Ġshould Ġalso Ġbe Ġcharacterized Ġby Ġhigh Ġvariability Ġwith Ġrespect Ġto Ġspeakers , Ġage Ġand Ġdialect s . ĠWhile Ġseveral Ġdatasets Ġwith Ġthese Ġproperties Ġare Ġavailable Ġfor Ġlanguages Ġlike ĠEnglish , ĠMandarin , ĠFrench , Ġthis Ġis Ġnot Ġthe Ġcase Ġfor Ġseveral Ġother Ġlanguages , Ġsuch Ġas ĠPersian . ĠTo Ġthis Ġend , Ġwe Ġproceeded Ġwith Ġcollecting Ġa Ġlarge - scale Ġdataset , Ġsuitable Ġfor Ġbuilding Ġrobust ĠAS R Ġmodels Ġin ĠPersian . </s> The Ġmain Ġgoal Ġof Ġthe ĠDeep Mine Ġproject Ġwas Ġto Ġcollect Ġspeech Ġfrom Ġat Ġleast Ġa Ġfew Ġthousand Ġspeakers , Ġenabling Ġresearch Ġand Ġdevelopment Ġof Ġdeep Ġlearning Ġmethods . ĠThe Ġproject Ġstarted Ġat Ġthe Ġbeginning Ġof Ġ2017 , Ġand Ġafter Ġdesigning Ġthe Ġdatabase Ġand Ġthe Ġdeveloping ĠAndroid Ġand Ġserver Ġapplications , Ġthe Ġdata Ġcollection Ġbegan Ġin Ġthe Ġmiddle Ġof Ġ2017 . ĠThe Ġproject Ġfinished Ġat Ġthe Ġend Ġof Ġ2018 Ġand Ġthe Ġcleaned - up Ġand Ġfinal Ġversion Ġof Ġthe Ġdatabase Ġwas Ġreleased Ġat Ġthe Ġbeginning Ġof Ġ2019 . ĠIn ĠB IB REF 4 , Ġthe Ġrunning Ġproject Ġand Ġits Ġdata Ġcollection Ġscenarios Ġwere Ġdescribed , Ġalongside Ġwith Ġsome Ġpreliminary Ġresults Ġand Ġstatistics . ĠIn Ġthis Ġpaper , Ġwe Ġannounce Ġthe Ġfinal Ġand Ġcleaned - up Ġversion Ġof Ġthe Ġdatabase , Ġdescribe Ġits Ġdifferent Ġparts Ġand Ġprovide Ġvarious Ġevaluation Ġsetups Ġfor Ġeach Ġpart . ĠFinally , Ġsince Ġthe Ġdatabase Ġwas Ġdesigned Ġmainly Ġfor Ġtext - dependent Ġspeaker Ġverification Ġpurposes , Ġsome Ġbaseline Ġresults Ġare Ġreported Ġfor Ġthis Ġtask Ġon Ġthe Ġofficial Ġevaluation Ġsetups . ĠAdditional Ġbaseline Ġresults Ġare Ġalso Ġreported Ġfor ĠPersian Ġspeech Ġrecognition . ĠHowever , Ġdue Ġto Ġthe Ġspace Ġlimitation Ġin Ġthis Ġpaper , Ġthe Ġbaseline Ġresults Ġare Ġnot Ġreported Ġfor Ġall Ġthe Ġdatabase Ġparts Ġand Ġconditions . ĠThey Ġwill Ġbe Ġdefined Ġand Ġreported Ġin Ġthe Ġdatabase Ġtechnical Ġdocumentation Ġand Ġin Ġa Ġfuture Ġjournal Ġpaper . </s> Data ĠCollection </s> Deep Mine Ġis Ġpublicly Ġavailable Ġfor Ġeverybody Ġwith Ġa Ġvariety Ġof Ġlicenses Ġfor Ġdifferent Ġusers . ĠIt Ġwas Ġcollected Ġusing Ġcrowds ourcing ĠB IB REF 4 . ĠThe Ġdata Ġcollection Ġwas Ġdone Ġusing Ġan ĠAndroid Ġapplication . ĠEach Ġrespondent Ġinstalled Ġthe Ġapplication Ġon Ġhis / her Ġpersonal Ġdevice Ġand Ġrecorded Ġseveral Ġphrases Ġin Ġdifferent Ġsessions . ĠThe ĠAndroid Ġapplication Ġdid Ġvarious Ġchecks Ġon Ġeach Ġutter ance Ġand Ġif Ġit Ġpassed Ġall Ġof Ġthem , Ġthe Ġrespondent Ġwas Ġdirected Ġto Ġthe Ġnext Ġphrase . ĠFor Ġmore Ġinformation Ġabout Ġdata Ġcollection Ġscenario , Ġplease Ġrefer Ġto ĠB IB REF 4 . </s> Data ĠCollection Ġ: :: ĠPost - Process ing </s> In Ġorder Ġto Ġclean - up Ġthe Ġdatabase , Ġthe Ġmain Ġpost - processing Ġstep Ġwas Ġto Ġfilter Ġout Ġproblematic Ġutter ances . ĠPossible Ġproblems Ġinclude Ġspeaker Ġword Ġinsert ions Ġ( e . g . Ġrepeating Ġsome Ġpart Ġof Ġa Ġphrase ), Ġdelet ions , Ġsubstit utions , Ġand Ġinvoluntary Ġdis flu encies . ĠTo Ġdetect Ġthese , Ġwe Ġimplemented Ġan Ġalignment Ġstage , Ġsimilar Ġto Ġthe Ġsecond Ġalignment Ġstage Ġin Ġthe ĠLib ri Spe ech Ġproject ĠB IB REF 5 . ĠIn Ġthis Ġmethod , Ġa Ġcustom Ġdecoding Ġgraph Ġwas Ġgenerated Ġfor Ġeach Ġphrase . ĠThe Ġdecoding Ġgraph Ġallows Ġfor Ġword Ġskipping Ġand Ġword Ġinsertion Ġin Ġthe Ġphrase . </s> For Ġtext - dependent Ġand Ġtext - prom pt ed Ġparts Ġof Ġthe Ġdatabase , Ġsuch Ġerrors Ġare Ġnot Ġallowed . ĠHence , Ġany Ġutter ances Ġwith Ġerrors Ġwere Ġremoved Ġfrom Ġthe Ġenrollment Ġand Ġtest Ġlists . ĠFor Ġthe Ġspeech Ġrecognition Ġpart , Ġa Ġsub - part Ġof Ġthe Ġutter ance Ġwhich Ġis Ġcorrectly Ġaligned Ġto Ġthe Ġcorresponding Ġtranscription Ġis Ġkept . ĠAfter Ġthe Ġcleaning Ġstep , Ġaround Ġ190 Ġthousand Ġutter ances Ġwith Ġfull Ġtranscription Ġand Ġ10 Ġthousand Ġwith Ġsub - part Ġalignment Ġhave Ġremained Ġin Ġthe Ġdatabase . </s> Data ĠCollection Ġ: :: ĠStatistics </s> After Ġprocessing Ġthe Ġdatabase Ġand Ġremoving Ġproblematic Ġrespondents Ġand Ġutter ances , Ġ1969 Ġrespondents Ġremained Ġin Ġthe Ġdatabase , Ġwith Ġ11 49 Ġof Ġthem Ġbeing Ġmale Ġand Ġ820 Ġfemale . Ġ297 Ġof Ġthe Ġrespondents Ġcould Ġnot Ġread ĠEnglish Ġand Ġhave Ġtherefore Ġread Ġonly Ġthe ĠPersian Ġprompts . ĠAbout Ġ13 200 Ġsessions Ġwere Ġrecorded Ġby Ġfemales Ġand Ġsimilarly , Ġabout Ġ9 500 Ġsessions Ġby Ġmales , Ġi . e . Ġwomen Ġare Ġover - represented Ġin Ġterms Ġof Ġsessions , Ġeven Ġthough Ġtheir Ġnumber Ġis Ġ17 % Ġsmaller Ġthan Ġthat Ġof Ġmales . ĠOther Ġuseful Ġstatistics Ġrelated Ġto Ġthe Ġdatabase Ġare Ġshown Ġin ĠTable ĠT AB REF 4 . </s> The Ġlast Ġstatus Ġof Ġthe Ġdatabase , Ġas Ġwell Ġas Ġother Ġrelated Ġand Ġuseful Ġinformation Ġabout Ġits Ġavailability Ġcan Ġbe Ġfound Ġon Ġits Ġwebsite , Ġtogether Ġwith Ġa Ġlimited Ġnumber Ġof Ġsamples . </s> Deep Mine ĠDatabase ĠParts </s> The ĠDeep Mine Ġdatabase Ġconsists Ġof Ġthree Ġparts . ĠThe Ġfirst Ġone Ġcontains Ġfixed Ġcommon Ġphrases Ġto Ġperform Ġtext - dependent Ġspeaker Ġverification . ĠThe Ġsecond Ġpart Ġconsists Ġof Ġrandom Ġsequences Ġof Ġwords Ġuseful Ġfor Ġtext - prom pt ed Ġspeaker Ġverification , Ġand Ġthe Ġlast Ġpart Ġincludes Ġphrases Ġwith Ġword - Ġand Ġphon eme - level Ġtranscription , Ġuseful Ġfor Ġtext - independent Ġspeaker Ġverification Ġusing Ġa Ġrandom Ġphrase Ġ( similar Ġto ĠPart 4 Ġof ĠRed D ots ). ĠThis Ġpart Ġcan Ġalso Ġserve Ġfor ĠPersian ĠAS R Ġtraining . ĠEach Ġpart Ġis Ġdescribed Ġin Ġmore Ġdetails Ġbelow . ĠTable ĠT AB REF 11 Ġshows Ġthe Ġnumber Ġof Ġunique Ġphrases Ġin Ġeach Ġpart Ġof Ġthe Ġdatabase . ĠFor Ġthe ĠEnglish Ġtext - dependent Ġpart , Ġthe Ġfollowing Ġphrases Ġwere Ġselected Ġfrom Ġpart 1 Ġof Ġthe ĠRed D ots Ġdatabase , Ġhence Ġthe ĠRed D ots Ġcan Ġbe Ġused Ġas Ġan Ġadditional Ġtraining Ġset Ġfor Ġthis Ġpart : </s> âĢ ľ My Ġvoice Ġis Ġmy Ġpassword . âĢ Ŀ </s> âĢ ľ OK ĠGoogle . âĢ Ŀ </s> âĢ ľ Art ificial Ġintelligence Ġis Ġfor Ġreal . âĢ Ŀ </s> âĢ ľ A ctions Ġspeak Ġlouder Ġthan Ġwords . âĢ Ŀ </s> âĢ ľ There Ġis Ġno Ġsuch Ġthing Ġas Ġa Ġfree Ġlunch . âĢ Ŀ </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 1 Ġ- ĠText - dependent Ġ( TD ) </s> This Ġpart Ġcontains Ġa Ġset Ġof Ġfixed Ġphrases Ġwhich Ġare Ġused Ġto Ġverify Ġspeakers Ġin Ġtext - dependent Ġmode . ĠEach Ġspeaker Ġut ters Ġ5 ĠPersian Ġphrases , Ġand Ġif Ġthe Ġspeaker Ġcan Ġread ĠEnglish , Ġ5 Ġphrases Ġselected Ġfrom ĠPart 1 Ġof Ġthe ĠRed D ots Ġdatabase Ġare Ġalso Ġrecorded . </s> We Ġhave Ġcreated Ġthree Ġexperimental Ġsetups Ġwith Ġdifferent Ġnumbers Ġof Ġspeakers Ġin Ġthe Ġevaluation Ġset . ĠFor Ġeach Ġsetup , Ġspeakers Ġwith Ġmore Ġrecording Ġsessions Ġare Ġincluded Ġin Ġthe Ġevaluation Ġset Ġand Ġthe Ġrest Ġof Ġthe Ġspeakers Ġare Ġused Ġfor Ġtraining Ġin Ġthe Ġbackground Ġset Ġ( in Ġthe Ġdatabase , Ġall Ġbackground Ġsets Ġare Ġbasically Ġtraining Ġdata ). ĠThe Ġrows Ġin ĠTable ĠT AB REF 13 Ġcorresponds Ġto Ġthe Ġdifferent Ġexperimental Ġsetups Ġand Ġshows Ġthe Ġnumbers Ġof Ġspeakers Ġin Ġeach Ġset . ĠNote Ġthat , Ġfor ĠEnglish , Ġwe Ġhave Ġfiltered Ġthe Ġ( Pers ian Ġnative ) Ġspeakers Ġby Ġthe Ġability Ġto Ġread ĠEnglish . ĠTherefore , Ġthere Ġare Ġfewer Ġspeakers Ġin Ġeach Ġset Ġfor ĠEnglish Ġthan Ġfor ĠPersian . ĠThere Ġis Ġa Ġsmall ĠâĢ ľ dev âĢ Ŀ Ġset Ġin Ġeach Ġsetup Ġwhich Ġcan Ġbe Ġused Ġfor Ġparameter Ġtuning Ġto Ġprevent Ġover - tun ing Ġon Ġthe Ġevaluation Ġset . </s> For Ġeach Ġexperimental Ġsetup , Ġwe Ġhave Ġdefined Ġseveral Ġofficial Ġtrial Ġlists Ġwith Ġdifferent Ġnumbers Ġof Ġenrollment Ġutter ances Ġper Ġtrial Ġin Ġorder Ġto Ġinvestigate Ġthe Ġeffects Ġof Ġhaving Ġdifferent Ġamounts Ġof Ġenrollment Ġdata . ĠAll Ġtrials Ġin Ġone Ġtrial Ġlist Ġhave Ġthe Ġsame Ġnumber Ġof Ġenrollment Ġutter ances Ġ( 3 Ġto Ġ6 ) Ġand Ġonly Ġone Ġtest Ġutter ance . ĠAll Ġenrollment Ġutter ances Ġin Ġa Ġtrial Ġare Ġtaken Ġfrom Ġdifferent Ġconsecutive Ġsessions Ġand Ġthe Ġtest Ġutter ance Ġis Ġtaken Ġfrom Ġyet Ġanother Ġsession . ĠFrom Ġall Ġthe Ġsetups Ġand Ġconditions , Ġthe Ġ100 - sp k Ġwith Ġ3 - session Ġenrollment Ġ( 3 - s ess ) Ġis Ġconsidered Ġas Ġthe Ġmain Ġevaluation Ġcondition . ĠIn ĠTable ĠT AB REF 14 , Ġthe Ġnumber Ġof Ġtrials Ġfor ĠPersian Ġ3 - s ess Ġare Ġshown Ġfor Ġthe Ġdifferent Ġtypes Ġof Ġtrial Ġin Ġthe Ġtext - dependent Ġspeaker Ġverification Ġ( S V ). ĠNote Ġthat Ġfor ĠImp oster - Wr ong Ġ( I W ) Ġtrials Ġ( i . e . Ġimp oster Ġspeaker Ġpron ouncing Ġwrong Ġphrase ), Ġwe Ġmerely Ġcreate Ġone Ġwrong Ġtrial Ġfor Ġeach ĠImp oster - Correct Ġ( IC ) Ġtrial Ġto Ġlimit Ġthe Ġhuge Ġnumber Ġof Ġpossible Ġtrials Ġfor Ġthis Ġcase . ĠSo , Ġthe Ġnumber Ġof Ġtrials Ġfor ĠIC Ġand ĠI W Ġcases Ġare Ġthe Ġsame . </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 2 Ġ- ĠText - prom pt ed Ġ( TP ) </s> For Ġthis Ġpart , Ġin Ġeach Ġsession , Ġ3 Ġrandom Ġsequences Ġof ĠPersian Ġmonth Ġnames Ġare Ġshown Ġto Ġthe Ġrespondent Ġin Ġtwo Ġmodes : ĠIn Ġthe Ġfirst Ġmode , Ġthe Ġsequence Ġconsists Ġof Ġall Ġ12 Ġmonths , Ġwhich Ġwill Ġbe Ġused Ġfor Ġspeaker Ġenrollment . ĠThe Ġsecond Ġmode Ġcontains Ġa Ġsequence Ġof Ġ3 Ġmonth Ġnames Ġthat Ġwill Ġbe Ġused Ġas Ġa Ġtest Ġutter ance . ĠIn Ġeach Ġ8 Ġsessions Ġreceived Ġby Ġa Ġrespondent Ġfrom Ġthe Ġserver , Ġthere Ġare Ġ3 Ġenrollment Ġphrases Ġof Ġall Ġ12 Ġmonths Ġ( all Ġin Ġjust Ġone Ġsession ), Ġand Ġ$ 7 Ġ\ times Ġ3 $ Ġother Ġtest Ġphrases , Ġcontaining Ġfewer Ġwords . ĠFor Ġa Ġrespondent Ġwho Ġcan Ġread ĠEnglish , Ġ3 Ġrandom Ġsequences Ġof ĠEnglish Ġdigits Ġare Ġalso Ġrecorded Ġin Ġeach Ġsession . ĠIn Ġone Ġof Ġthe Ġsessions , Ġthese Ġsequences Ġcontain Ġall Ġdigits Ġand Ġthe Ġremaining Ġones Ġcontain Ġonly Ġ4 Ġdigits . </s> Similar Ġto Ġthe Ġtext - dependent Ġcase , Ġthree Ġexperimental Ġsetups Ġwith Ġdifferent Ġnumber Ġof Ġspeaker Ġin Ġthe Ġevaluation Ġset Ġare Ġdefined Ġ( cor respond ing Ġto Ġthe Ġrows Ġin ĠTable ĠT AB REF 16 ). ĠHowever , Ġdifferent Ġstrategy Ġis Ġused Ġfor Ġdefining Ġtrials : ĠDepending Ġon Ġthe Ġenrollment Ġcondition Ġ( 1 - Ġto Ġ3 - s ess ), Ġtrials Ġare Ġenrolled Ġon Ġutter ances Ġof Ġall Ġwords Ġfrom Ġ1 Ġto Ġ3 Ġdifferent Ġsessions Ġ( i . e . Ġ3 Ġto Ġ9 Ġutter ances ). ĠFurther , Ġwe Ġconsider Ġtwo Ġconditions Ġfor Ġtest Ġutter ances : Ġseq Ġtest Ġutter ance Ġwith Ġonly Ġ3 Ġor Ġ4 Ġwords Ġand Ġfull Ġtest Ġutter ances Ġwith Ġall Ġwords Ġ( i . e . Ġsame Ġwords Ġas Ġin Ġenrollment Ġbut Ġin Ġdifferent Ġorder ). ĠFrom Ġall Ġsetups Ġan Ġall Ġconditions , Ġthe Ġ100 - sp k Ġwith Ġ1 - session Ġenrol ment Ġ( 1 - s ess ) Ġis Ġconsidered Ġas Ġthe Ġmain Ġevaluation Ġcondition Ġfor Ġthe Ġtext - prom pt ed Ġcase . ĠIn ĠTable ĠT AB REF 16 , Ġthe Ġnumbers Ġof Ġtrials Ġ( sum Ġfor Ġboth Ġseq Ġand Ġfull Ġconditions ) Ġfor ĠPersian Ġ1 - s ess Ġare Ġshown Ġfor Ġthe Ġdifferent Ġtypes Ġof Ġtrials Ġin Ġthe Ġtext - prom pt ed ĠSV . ĠAgain , Ġwe Ġjust Ġcreate Ġone ĠI W Ġtrial Ġfor Ġeach ĠIC Ġtrial . </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 3 Ġ- ĠText - independent Ġ( TI ) </s> In Ġthis Ġpart , Ġ8 ĠPersian Ġphrases Ġthat Ġhave Ġalready Ġbeen Ġtransc ribed Ġon Ġthe Ġphone Ġlevel Ġare Ġdisplayed Ġto Ġthe Ġrespondent . ĠThese Ġphrases Ġare Ġchosen Ġmostly Ġfrom Ġnews Ġand ĠPersian ĠWikipedia . ĠIf Ġthe Ġrespondent Ġis Ġunable Ġto Ġread ĠEnglish , Ġinstead Ġof Ġ5 Ġfixed Ġphrases Ġand Ġ3 Ġrandom Ġdigit Ġstrings , Ġ8 Ġother ĠPersian Ġphrases Ġare Ġalso Ġprompted Ġto Ġthe Ġrespondent Ġto Ġhave Ġexactly Ġ24 Ġphrases Ġin Ġeach Ġrecording Ġsession . </s> This Ġpart Ġcan Ġbe Ġuseful Ġat Ġleast Ġfor Ġthree Ġpotential Ġapplications . ĠFirst , Ġit Ġcan Ġbe Ġused Ġfor Ġtext - independent Ġspeaker Ġverification . ĠThe Ġsecond Ġapplication Ġof Ġthis Ġpart Ġ( same Ġas ĠPart 4 Ġof ĠRed D ots ) Ġis Ġtext - prom pt ed Ġspeaker Ġverification Ġusing Ġrandom Ġtext Ġ( instead Ġof Ġa Ġrandom Ġsequence Ġof Ġwords ). ĠFinally , Ġthe Ġthird Ġapplication Ġis Ġlarge Ġvocabulary Ġspeech Ġrecognition Ġin ĠPersian Ġ( expl ained Ġin Ġthe Ġnext Ġsub - section ). </s> Based Ġon Ġthe Ġrecording Ġsessions , Ġwe Ġcreated Ġtwo Ġexperimental Ġsetups Ġfor Ġspeaker Ġverification . ĠIn Ġthe Ġfirst Ġone , Ġrespondents Ġwith Ġat Ġleast Ġ17 Ġrecording Ġsessions Ġare Ġincluded Ġto Ġthe Ġevaluation Ġset , Ġrespondents Ġwith Ġ16 Ġsessions Ġto Ġthe Ġdevelopment Ġand Ġthe Ġrest Ġof Ġrespondents Ġto Ġthe Ġbackground Ġset Ġ( can Ġbe Ġused Ġas Ġtraining Ġdata ). ĠIn Ġthe Ġsecond Ġsetup , Ġrespondents Ġwith Ġat Ġleast Ġ8 Ġsessions Ġare Ġincluded Ġto Ġthe Ġevaluation Ġset , Ġrespondents Ġwith Ġ6 Ġor Ġ7 Ġsessions Ġto Ġthe Ġdevelopment Ġand Ġthe Ġrest Ġof Ġrespondents Ġto Ġthe Ġbackground Ġset . ĠTable ĠT AB REF 18 Ġshows Ġnumbers Ġof Ġspeakers Ġin Ġeach Ġset Ġof Ġthe Ġdatabase Ġfor Ġtext - independent ĠSV Ġcase . </s> For Ġtext - independent ĠSV , Ġwe Ġhave Ġconsidered Ġ4 Ġscenarios Ġfor Ġenrollment Ġand Ġ4 Ġscenarios Ġfor Ġtest . ĠThe Ġspeaker Ġcan Ġbe Ġenrolled Ġusing Ġutter ances Ġfrom Ġ1 , Ġ2 Ġor Ġ3 Ġconsecutive Ġsessions Ġ( 1 s ess Ġto Ġ3 s ess ) Ġor Ġusing Ġ8 Ġutter ances Ġfrom Ġ8 Ġdifferent Ġsessions . ĠThe Ġtest Ġspeech Ġcan Ġbe Ġone Ġutter ance Ġ( 1 utt ) Ġfor Ġshort Ġduration Ġscenario Ġor Ġall Ġutter ances Ġin Ġone Ġsession Ġ( 1 s ess ) Ġfor Ġlong Ġduration Ġcase . ĠIn Ġaddition , Ġtest Ġspeech Ġcan Ġbe Ġselected Ġfrom Ġ5 ĠEnglish Ġphrases Ġfor Ġcross - language Ġtesting Ġ( en rollment Ġusing ĠPersian Ġutter ances Ġand Ġtest Ġusing ĠEnglish Ġutter ances ). ĠFrom Ġall Ġsetups , Ġ1 s ess - 1 utt Ġand Ġ1 s ess - 1 s ess Ġfor Ġ4 38 - sp k Ġset Ġare Ġconsidered Ġas Ġthe Ġmain Ġevaluation Ġsetups Ġfor Ġtext - independent Ġcase . ĠTable ĠT AB REF 19 Ġshows Ġnumber Ġof Ġtrials Ġfor Ġthese Ġsetups . </s> For Ġtext - prom pt ed ĠSV Ġwith Ġrandom Ġtext , Ġthe Ġsame Ġsetup Ġas Ġtext - independent Ġcase Ġtogether Ġwith Ġcorresponding Ġutter ance Ġtranscript ions Ġcan Ġbe Ġused . </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 3 Ġ- ĠSpeech ĠRecogn ition </s> As Ġexplained Ġbefore , ĠPart 3 Ġof Ġthe ĠDeep Mine Ġdatabase Ġcan Ġbe Ġused Ġfor ĠPersian Ġread Ġspeech Ġrecognition . ĠThere Ġare Ġonly Ġa Ġfew Ġdatabases Ġfor Ġspeech Ġrecognition Ġin ĠPersian ĠB IB REF 6 , ĠB IB REF 7 . ĠHence , Ġthis Ġpart Ġcan Ġat Ġleast Ġpartly Ġaddress Ġthis Ġproblem Ġand Ġenable Ġrobust Ġspeech Ġrecognition Ġapplications Ġin ĠPersian . ĠAdditionally , Ġit Ġcan Ġbe Ġused Ġfor Ġspeaker Ġrecognition Ġapplications , Ġsuch Ġas Ġtraining Ġdeep Ġneural Ġnetworks Ġ( D NN s ) Ġfor Ġextracting Ġbottleneck Ġfeatures ĠB IB REF 8 , Ġor Ġfor Ġcollecting Ġsufficient Ġstatistics Ġusing ĠD NN s Ġfor Ġi - vector Ġtraining . </s> We Ġhave Ġrandomly Ġselected Ġ50 Ġspeakers Ġ( 25 Ġfor Ġeach Ġgender ) Ġfrom Ġthe Ġall Ġspeakers Ġin Ġthe Ġdatabase Ġwhich Ġhave Ġnet Ġspeech Ġ( without Ġsilence Ġparts ) Ġbetween Ġ25 Ġminutes Ġto Ġ50 Ġminutes Ġas Ġtest Ġspeakers . ĠFor Ġeach Ġspeaker , Ġthe Ġutter ances Ġin Ġthe Ġfirst Ġ5 Ġsessions Ġare Ġincluded Ġto Ġ( small ) Ġtest - set Ġand Ġthe Ġother Ġutter ances Ġof Ġtest Ġspeakers Ġare Ġconsidered Ġas Ġa Ġlarge - test - set . ĠThe Ġremaining Ġutter ances Ġof Ġthe Ġother Ġspeakers Ġare Ġincluded Ġin Ġthe Ġtraining Ġset . ĠThe Ġtest - set , Ġlarge - test - set Ġand Ġtrain - set Ġcontain Ġ5 . 9 , Ġ28 . 5 Ġand Ġ450 Ġhours Ġof Ġspeech Ġrespectively . </s> There Ġare Ġabout Ġ8 300 Ġutter ances Ġin ĠPart 3 Ġwhich Ġcontain Ġonly ĠPersian Ġfull Ġnames Ġ( i . e . Ġfirst Ġand Ġfamily Ġname Ġpairs ). ĠEach Ġphrase Ġconsists Ġof Ġseveral Ġfull Ġnames Ġand Ġtheir Ġphon eme Ġtranscript ions Ġwere Ġextracted Ġautomatically Ġusing Ġa Ġtrained ĠG rap he me - to - Ph on eme Ġ( G 2 P ). ĠThese Ġutter ances Ġcan Ġbe Ġused Ġto Ġevaluate Ġthe Ġperformance Ġof Ġa Ġsystems Ġfor Ġname Ġrecognition , Ġwhich Ġis Ġusually Ġmore Ġdifficult Ġthan Ġthe Ġnormal Ġspeech Ġrecognition Ġbecause Ġof Ġthe Ġlack Ġof Ġa Ġreliable Ġlanguage Ġmodel . </s> Exper iments Ġand ĠResults </s> Due Ġto Ġthe Ġspace Ġlimitation , Ġwe Ġpresent Ġresults Ġonly Ġfor Ġthe ĠPersian Ġtext - dependent Ġspeaker Ġverification Ġand Ġspeech Ġrecognition . </s> Exper iments Ġand ĠResults Ġ: :: ĠSpeaker ĠVer ification ĠExper iments </s> We Ġconducted Ġan Ġexperiment Ġon Ġtext - dependent Ġspeaker Ġverification Ġpart Ġof Ġthe Ġdatabase , Ġusing Ġthe Ġi - vector Ġbased Ġmethod Ġproposed Ġin ĠB IB REF 9 , ĠB IB REF 10 Ġand Ġapplied Ġit Ġto Ġthe ĠPersian Ġportion Ġof ĠPart 1 . ĠIn Ġthis Ġexperiment , Ġ20 - dimensional ĠMF CC Ġfeatures Ġalong Ġwith Ġfirst Ġand Ġsecond Ġderivatives Ġare Ġextracted Ġfrom Ġ16 ĠkHz Ġsignals Ġusing ĠHT K ĠB IB REF 11 Ġwith Ġ25 Ġms ĠHam ming Ġwindow ed Ġframes Ġwith Ġ15 Ġms Ġoverlap . </s> The Ġreported Ġresults Ġare Ġobtained Ġwith Ġa Ġ400 - dimensional Ġgender Ġindependent Ġi - vector Ġbased Ġsystem . ĠThe Ġi - ve ctors Ġare Ġfirst Ġlength - normal ized Ġand Ġare Ġfurther Ġnormalized Ġusing Ġphrase - Ġand Ġgender - dependent ĠRegular ized ĠWithin - Class ĠCov ari ance ĠNormal ization Ġ( RW CC N ) ĠB IB REF 10 . ĠCos ine Ġdistance Ġis Ġused Ġto Ġobtain Ġspeaker Ġverification Ġscores Ġand Ġphrase - Ġand Ġgender - dependent Ġs - norm Ġis Ġused Ġfor Ġnormal izing Ġthe Ġscores . ĠFor Ġalign ing Ġspeech Ġframes Ġto ĠGa ussian Ġcomponents , Ġmon ophone ĠHM Ms Ġwith Ġ3 Ġstates Ġand Ġ8 ĠGa ussian Ġcomponents Ġin Ġeach Ġstate Ġare Ġused ĠB IB REF 10 . ĠWe Ġonly Ġmodel Ġthe Ġphon em es Ġwhich Ġappear Ġin Ġthe Ġ5 ĠPersian Ġtext - dependent Ġphrases . </s> For Ġspeaker Ġverification Ġexperiments , Ġthe Ġresults Ġwere Ġreported Ġin Ġterms Ġof ĠEqual ĠError ĠRate Ġ( E ER ) Ġand ĠNormal ized ĠDetection ĠCost ĠFunction Ġas Ġdefined Ġfor ĠN IST ĠS RE 08 Ġ($ \ math rm Ġ{ ND CF _{ 0 . 01 } ^{ min }} $ ) Ġand ĠN IST ĠS RE 10 Ġ($ \ math rm Ġ{ ND CF _{ 0 . 001 } ^{ min }} $ ). ĠAs Ġshown Ġin ĠTable ĠT AB REF 22 , Ġin Ġtext - dependent ĠSV Ġthere Ġare Ġ4 Ġtypes Ġof Ġtrials : ĠTarget - Correct Ġand ĠImp oster - Correct Ġrefer Ġto Ġtrials Ġwhen Ġthe Ġpass - phrase Ġis Ġuttered Ġcorrectly Ġby Ġtarget Ġand Ġimp oster Ġspeakers Ġrespectively , Ġand Ġin Ġsame Ġmanner , ĠTarget - Wr ong Ġand ĠImp oster - Wr ong Ġrefer Ġto Ġtrials Ġwhen Ġspeakers Ġuttered Ġa Ġwrong Ġpass - phrase . ĠIn Ġthis Ġpaper , Ġonly Ġthe Ġcorrect Ġtrials Ġ( i . e . ĠTarget - Correct Ġas Ġtarget Ġtrials Ġvs ĠImp oster - Correct Ġas Ġnon - target Ġtrials ) Ġare Ġconsidered Ġfor Ġevaluating Ġsystems Ġas Ġit Ġhas Ġbeen Ġproved Ġthat Ġthese Ġare Ġthe Ġmost Ġchallenging Ġtrials Ġin Ġtext - dependent ĠSV ĠB IB REF 8 , ĠB IB REF 12 . </s> Table ĠT AB REF 23 Ġshows Ġthe Ġresults Ġof Ġtext - dependent Ġexperiments Ġusing ĠPersian Ġ100 - sp k Ġand Ġ3 - s ess Ġsetup . ĠFor Ġfiltering Ġtrials , Ġthe Ġrespondents ' Ġmobile Ġbrand Ġand Ġmodel Ġwere Ġused Ġin Ġthis Ġexperiment . ĠIn Ġthe Ġtable , Ġthe Ġfirst Ġtwo Ġletters Ġin Ġthe Ġfilter Ġnotation Ġrelate Ġto Ġthe Ġtarget Ġtrials Ġand Ġthe Ġsecond Ġtwo Ġletters Ġ( i . e . Ġright Ġside Ġof Ġthe Ġcolon ) Ġrelate Ġfor Ġnon - target Ġtrials . ĠFor Ġtarget Ġtrials , Ġthe Ġfirst ĠY Ġmeans Ġthe Ġenrol ment Ġand Ġtest Ġutter ances Ġwere Ġrecorded Ġusing Ġa Ġdevice Ġwith Ġthe Ġsame Ġbrand Ġby Ġthe Ġtarget Ġspeaker . ĠThe Ġsecond ĠY Ġletter Ġmeans Ġboth Ġrecordings Ġwere Ġdone Ġusing Ġexactly Ġthe Ġsame Ġdevice Ġmodel . ĠSimilarly , Ġthe Ġfirst ĠY Ġfor Ġnon - target Ġtrials Ġmeans Ġthat Ġthe Ġdevices Ġof Ġtarget Ġand Ġimp oster Ġspeakers Ġare Ġfrom Ġthe Ġsame Ġbrand Ġ( i . e . Ġmanufacturer ). ĠThe Ġsecond ĠY Ġmeans Ġthat , Ġin Ġaddition Ġto Ġthe Ġsame Ġbrand , Ġboth Ġdevices Ġhave Ġthe Ġsame Ġmodel . ĠSo , Ġthe Ġmost Ġdifficult Ġtarget Ġtrials Ġare ĠâĢ ľ NN âĢ Ŀ , Ġwhere Ġthe Ġspeaker Ġhas Ġused Ġdifferent Ġa Ġdevice Ġat Ġthe Ġtest Ġtime . ĠIn Ġthe Ġsame Ġmanner , Ġthe Ġmost Ġdifficult Ġnon - target Ġtrials Ġwhich Ġshould Ġbe Ġrejected Ġby Ġthe Ġsystem Ġare ĠâĢ ľ YY âĢ Ŀ Ġwhere Ġthe Ġimp oster Ġspeaker Ġhas Ġused Ġthe Ġsame Ġdevice Ġmodel Ġas Ġthe Ġtarget Ġspeaker Ġ( note Ġthat Ġit Ġdoes Ġnot Ġmean Ġphysically Ġthe Ġsame Ġdevice Ġbecause Ġeach Ġspeaker Ġparticipated Ġin Ġthe Ġproject Ġusing Ġa Ġpersonal Ġmobile Ġdevice ). ĠHence , Ġthe Ġsimilarity Ġin Ġthe Ġrecording Ġchannel Ġmakes Ġrejection Ġmore Ġdifficult . </s> The Ġfirst Ġrow Ġin ĠTable ĠT AB REF 23 Ġshows Ġthe Ġresults Ġfor Ġall Ġtrials . ĠBy Ġcomparing Ġthe Ġresults Ġwith Ġthe Ġbest Ġpublished Ġresults Ġon ĠR SR 2015 Ġand ĠRed D ots ĠB IB REF 10 , ĠB IB REF 8 , ĠB IB REF 12 , Ġit Ġis Ġclear Ġthat Ġthe ĠDeep Mine Ġdatabase Ġis Ġmore Ġchallenging Ġthan Ġboth ĠR SR 2015 Ġand ĠRed D ots Ġdatabases . ĠFor ĠR SR 2015 , Ġthe Ġsame Ġi - vector / H MM - based Ġmethod Ġwith Ġboth ĠRW CC N Ġand Ġs - norm Ġhas Ġachieved ĠE ER Ġless Ġthan Ġ0 . 3 % Ġfor Ġboth Ġgenders Ġ( Table ĠVI Ġin ĠB IB REF 10 ). ĠThe Ġconventional ĠRe lev ance ĠMAP Ġadaptation Ġwith ĠH MM Ġalignment Ġwithout Ġapplying Ġany Ġchannel - comp ensation Ġtechniques Ġ( i . e . Ġwithout Ġapplying ĠRW CC N Ġand Ġs - norm Ġdue Ġto Ġthe Ġlack Ġof Ġsuitable Ġtraining Ġdata ) Ġon ĠRed D ots ĠPart 1 Ġfor Ġthe Ġmale Ġhas Ġachieved ĠE ER Ġaround Ġ1 . 5 % Ġ( Table ĠXI Ġin ĠB IB REF 10 ). ĠIt Ġis Ġworth Ġnoting Ġthat ĠE ER s Ġfor ĠDeep Mine Ġdatabase Ġwithout Ġany Ġchannel - comp ensation Ġtechniques Ġare Ġ2 . 1 Ġand Ġ3 . 7 % Ġfor Ġmales Ġand Ġfemales Ġrespectively . </s> One Ġinteresting Ġadvantage Ġof Ġthe ĠDeep Mine Ġdatabase Ġcompared Ġto Ġboth ĠR SR 2015 Ġand ĠRed D ots Ġis Ġhaving Ġseveral Ġtarget Ġspeakers Ġwith Ġmore Ġthan Ġone Ġmobile Ġdevice . ĠThis Ġis Ġallows Ġus Ġto Ġanalyse Ġthe Ġeffects Ġof Ġchannel Ġcompensation Ġmethods . ĠThe Ġsecond Ġrow Ġin ĠTable ĠT AB REF 23 Ġcorresponds Ġto Ġthe Ġmost Ġdifficult Ġtrials Ġwhere Ġthe Ġtarget Ġtrials Ġcome Ġfrom Ġmobile Ġdevices Ġwith Ġdifferent Ġmodels Ġwhile Ġimp oster Ġtrials Ġcome Ġfrom Ġthe Ġsame Ġdevice Ġmodels . ĠIt Ġis Ġclear Ġthat Ġsevere Ġdegradation Ġwas Ġcaused Ġby Ġthis Ġkind Ġof Ġchannel Ġeffects Ġ( i . e . Ġdecreasing Ġwithin - spe aker Ġsimilarities Ġwhile Ġincreasing Ġbetween - spe aker Ġsimilarities ), Ġespecially Ġfor Ġfemales . </s> The Ġresults Ġin Ġthe Ġthird Ġrow Ġshow Ġthe Ġcondition Ġwhen Ġtarget Ġspeakers Ġat Ġthe Ġtest Ġtime Ġuse Ġexactly Ġthe Ġsame Ġdevice Ġthat Ġwas Ġused Ġfor Ġenrollment . ĠComp aring Ġthis Ġrow Ġwith Ġthe Ġresults Ġin Ġthe Ġfirst Ġrow Ġproves Ġhow Ġmuch Ġimprovement Ġcan Ġbe Ġachieved Ġwhen Ġexactly Ġthe Ġsame Ġdevice Ġis Ġused Ġby Ġthe Ġtarget Ġspeaker . </s> The Ġresults Ġin Ġthe Ġfourth Ġrow Ġshow Ġthe Ġcondition Ġwhen Ġimp oster Ġspeakers Ġalso Ġuse Ġthe Ġsame Ġdevice Ġmodel Ġat Ġtest Ġtime Ġto Ġfool Ġthe Ġsystem . ĠSo , Ġin Ġthis Ġcase , Ġthere Ġis Ġno Ġdevice Ġmismatch Ġin Ġall Ġtrials . ĠBy Ġcomparing Ġthe Ġresults Ġwith Ġthe Ġthird Ġrow , Ġwe Ġcan Ġsee Ġhow Ġmuch Ġdegradation Ġis Ġcaused Ġif Ġwe Ġonly Ġconsider Ġthe Ġnon - target Ġtrials Ġwith Ġthe Ġsame Ġdevice . </s> The Ġfifth Ġrow Ġshows Ġsimilar Ġresults Ġwhen Ġthe Ġimp oster Ġspeakers Ġuse Ġdevice Ġof Ġthe Ġsame Ġbrand Ġas Ġthe Ġtarget Ġspeaker Ġbut Ġwith Ġa Ġdifferent Ġmodel . ĠSurprisingly , Ġin Ġthis Ġcase , Ġthe Ġdegradation Ġis Ġnegligible Ġand Ġit Ġmeans Ġthat Ġmob iles Ġfrom Ġa Ġspecific Ġbrand Ġ( manufact urer ) Ġhave Ġdifferent Ġrecording Ġchannel Ġproperties . </s> The Ġdegraded Ġfemale Ġresults Ġin Ġthe Ġsixth Ġrow Ġas Ġcompared Ġto Ġthe Ġthird Ġrow Ġshow Ġthe Ġeffect Ġof Ġusing Ġa Ġdifferent Ġdevice Ġmodel Ġfrom Ġthe Ġsame Ġbrand Ġfor Ġtarget Ġtrials . ĠFor Ġmales , Ġthe Ġfilters Ġbrings Ġalmost Ġthe Ġsame Ġsubs ets Ġof Ġtrials , Ġwhich Ġexplains Ġthe Ġvery Ġsimilar Ġresults Ġin Ġthis Ġcase . </s> Looking Ġat Ġthe Ġfirst Ġtwo Ġand Ġthe Ġlast Ġrow Ġof ĠTable ĠT AB REF 23 , Ġone Ġcan Ġnotice Ġthe Ġsignificantly Ġworse Ġperformance Ġobtained Ġfor Ġthe Ġfemale Ġtrials Ġas Ġcompared Ġto Ġmales . ĠNote Ġthat Ġthese Ġthree Ġrows Ġinclude Ġtarget Ġtrials Ġwhere Ġthe Ġdevices Ġused Ġfor Ġenrollment Ġdo Ġnot Ġnecessarily Ġmatch Ġthe Ġdevices Ġused Ġfor Ġrecording Ġtest Ġutter ances . ĠOn Ġthe Ġother Ġhand , Ġin Ġrows Ġ3 Ġto Ġ6 , Ġwhich Ġexclude Ġsuch Ġmism atched Ġtrials , Ġthe Ġperformance Ġfor Ġmales Ġand Ġfemales Ġis Ġcomparable . ĠThis Ġsuggest Ġthat Ġthe Ġdegraded Ġresults Ġfor Ġfemales Ġare Ġcaused Ġby Ġsome Ġproblematic Ġtrials Ġwith Ġdevice Ġmismatch . ĠThe Ġexact Ġreason Ġfor Ġthis Ġdegradation Ġis Ġso Ġfar Ġunclear Ġand Ġneeds Ġa Ġfurther Ġinvestigation . </s> In Ġthe Ġlast Ġrow Ġof Ġthe Ġtable , Ġthe Ġcondition Ġof Ġthe Ġsecond Ġrow Ġis Ġrelaxed : Ġthe Ġtarget Ġdevice Ġshould Ġhave Ġdifferent Ġmodel Ġpossibly Ġfrom Ġthe Ġsame Ġbrand Ġand Ġimp oster Ġdevice Ġonly Ġneeds Ġto Ġbe Ġfrom Ġthe Ġsame Ġbrand . ĠIn Ġthis Ġcase , Ġas Ġwas Ġexpected , Ġthe Ġperformance Ġdegradation Ġis Ġsmaller Ġthan Ġin Ġthe Ġsecond Ġrow . </s> Exper iments Ġand ĠResults Ġ: :: ĠSpeech ĠRecogn ition ĠExper iments </s> In Ġaddition Ġto Ġspeaker Ġverification , Ġwe Ġpresent Ġseveral Ġspeech Ġrecognition Ġexperiments Ġon ĠPart 3 . ĠThe Ġexperiments Ġwere Ġperformed Ġwith Ġthe ĠK aldi Ġtool kit ĠB IB REF 13 . ĠFor Ġtraining ĠH MM - based ĠMono Phone Ġmodel , Ġonly Ġ20 Ġthousands Ġof Ġshortest Ġutter ances Ġare Ġused Ġand Ġfor Ġother Ġmodels Ġthe Ġwhole Ġtraining Ġdata Ġis Ġused . ĠThe ĠD NN Ġbased Ġacoustic Ġmodel Ġis Ġa Ġtime - delay ĠD NN Ġwith Ġlow - rank Ġfactor ized Ġlayers Ġand Ġskip Ġconnections Ġwithout Ġi - vector Ġadaptation Ġ( a Ġmodified Ġnetwork Ġfrom Ġone Ġof Ġthe Ġbest Ġperforming ĠLib ri Spe ech Ġrecipes ). ĠThe Ġnetwork Ġis Ġshown Ġin ĠTable ĠT AB REF 25 : Ġthere Ġare Ġ16 ĠF - TD NN Ġlayers , Ġwith Ġdimension Ġ15 36 Ġand Ġlinear Ġbottleneck Ġlayers Ġof Ġdimension Ġ256 . ĠThe Ġacoustic Ġmodel Ġis Ġtrained Ġfor Ġ10 Ġepoch s Ġusing Ġlatt ice - free Ġmaximum Ġmutual Ġinformation Ġ( L F - M MI ) Ġwith Ġcross - ent ropy Ġregular ization ĠB IB REF 14 . ĠRe - scoring Ġis Ġdone Ġusing Ġa Ġpr uned Ġtrig ram Ġlanguage Ġmodel Ġand Ġthe Ġsize Ġof Ġthe Ġdictionary Ġis Ġaround Ġ90 , 000 Ġwords . </s> Table ĠT AB REF 26 Ġshows Ġthe Ġresults Ġin Ġterms Ġof Ġword Ġerror Ġrate Ġ( WER ) Ġfor Ġdifferent Ġevaluated Ġmethods . ĠAs Ġcan Ġbe Ġseen , Ġthe Ġcreated Ġdatabase Ġcan Ġbe Ġused Ġto Ġtrain Ġwell Ġperforming Ġand Ġpractically Ġusable ĠPersian ĠAS R Ġmodels . </s> Con clusions </s> In Ġthis Ġpaper , Ġwe Ġhave Ġdescribed Ġthe Ġfinal Ġversion Ġof Ġa Ġlarge Ġspeech Ġcorpus , Ġthe ĠDeep Mine Ġdatabase . ĠIt Ġhas Ġbeen Ġcollected Ġusing Ġcrowds ourcing Ġand , Ġaccording Ġto Ġthe Ġbest Ġof Ġour Ġknowledge , Ġit Ġis Ġthe Ġlargest Ġpublic Ġtext - dependent Ġand Ġtext - prom pt ed Ġspeaker Ġverification Ġdatabase Ġin Ġtwo Ġlanguages : ĠPersian Ġand ĠEnglish . ĠIn Ġaddition , Ġit Ġis Ġthe Ġlargest Ġtext - independent Ġspeaker Ġverification Ġevaluation Ġdatabase , Ġmaking Ġit Ġsuitable Ġto Ġrobust ly Ġevaluate Ġstate - of - the - art Ġmethods Ġon Ġdifferent Ġconditions . ĠAlong side Ġthese Ġappealing Ġproperties , Ġit Ġcomes Ġwith Ġphone - level Ġtranscription , Ġmaking Ġit Ġsuitable Ġto Ġtrain Ġdeep Ġneural Ġnetwork Ġmodels Ġfor ĠPersian Ġspeech Ġrecognition . </s> We Ġprovided Ġseveral Ġevaluation Ġprotocols Ġfor Ġeach Ġpart Ġof Ġthe Ġdatabase . ĠThe Ġprotocols Ġallow Ġresearchers Ġto Ġinvestigate Ġthe Ġperformance Ġof Ġdifferent Ġmethods Ġin Ġvarious Ġscenarios Ġand Ġstudy Ġthe Ġeffects Ġof Ġchannels , Ġduration Ġand Ġphrase Ġtext Ġon Ġthe Ġperformance . ĠWe Ġalso Ġprovide Ġtwo Ġtest Ġsets Ġfor Ġspeech Ġrecognition : ĠOne Ġnormal Ġtest Ġset Ġwith Ġa Ġfew Ġminutes Ġof Ġspeech Ġfor Ġeach Ġspeaker Ġand Ġone Ġlarge Ġtest Ġset Ġwith Ġmore Ġ( 30 Ġminutes Ġon Ġaverage ) Ġspeech Ġthat Ġcan Ġbe Ġused Ġfor Ġany Ġspeaker Ġadaptation Ġmethod . </s> As Ġbaseline Ġresults , Ġwe Ġreported Ġthe Ġperformance Ġof Ġan Ġi - vector / H MM Ġbased Ġmethod Ġon ĠPersian Ġtext - dependent Ġpart . ĠMoreover , Ġwe Ġconducted Ġspeech Ġrecognition Ġexperiments Ġusing Ġconventional ĠH MM - based Ġmethods , Ġas Ġwell Ġas Ġstate - of - the - art Ġdeep Ġneural Ġnetwork Ġbased Ġmethod Ġusing ĠK aldi Ġtool kit Ġwith Ġpromising Ġperformance . ĠText - dependent Ġresults Ġhave Ġshown Ġthat Ġthe ĠDeep Mine Ġdatabase Ġis Ġmore Ġchallenging Ġthan ĠR SR 2015 Ġand ĠRed D ots Ġdatabases . </s> Acknowled gments </s> The Ġdata Ġcollection Ġproject Ġwas Ġmainly Ġsupported Ġby ĠSharif ĠDeep Mine Ġcompany . ĠThe Ġwork Ġon Ġthe Ġpaper Ġwas Ġsupported Ġby ĠCzech ĠNational ĠScience ĠFoundation Ġ( G AC R ) Ġproject Ġ" NE URE M 3 " ĠNo . Ġ19 - 269 34 X Ġand Ġthe ĠNational ĠProgramme Ġof ĠS ustain ability Ġ( N PU ĠII ) Ġproject Ġ" IT 4 In nov ations Ġexcellence Ġin Ġscience Ġ- ĠL Q 16 02 ".
Introduction Nowadays deep learning techniques outperform the other conventional methods in most of the speech-related tasks. Training robust deep neural networks for each task depends on the availability of powerful processing GPUs, as well as standard and large scale datasets. In text-independent speaker verification, large-scale datasets are available, thanks to the NIST SRE evaluations and other data collection projects such as VoxCeleb BIBREF0. In text-dependent speaker recognition, experiments with end-to-end architectures conducted on large proprietary databases have demonstrated their superiority over traditional approaches BIBREF1. Yet, contrary to text-independent speaker recognition, text-dependent speaker recognition lacks large-scale publicly available databases. The two most well-known datasets are probably RSR2015 BIBREF2 and RedDots BIBREF3. The former contains speech data collected from 300 individuals in a controlled manner, while the latter is used primarily for evaluation rather than training, due to its small number of speakers (only 64). Motivated by this lack of large-scale dataset for text-dependent speaker verification, we chose to proceed with the collection of the DeepMine dataset, which we expect to become a standard benchmark for the task. Apart from speaker recognition, large amounts of training data are required also for training automatic speech recognition (ASR) systems. Such datasets should not only be large in size, they should also be characterized by high variability with respect to speakers, age and dialects. While several datasets with these properties are available for languages like English, Mandarin, French, this is not the case for several other languages, such as Persian. To this end, we proceeded with collecting a large-scale dataset, suitable for building robust ASR models in Persian. The main goal of the DeepMine project was to collect speech from at least a few thousand speakers, enabling research and development of deep learning methods. The project started at the beginning of 2017, and after designing the database and the developing Android and server applications, the data collection began in the middle of 2017. The project finished at the end of 2018 and the cleaned-up and final version of the database was released at the beginning of 2019. In BIBREF4, the running project and its data collection scenarios were described, alongside with some preliminary results and statistics. In this paper, we announce the final and cleaned-up version of the database, describe its different parts and provide various evaluation setups for each part. Finally, since the database was designed mainly for text-dependent speaker verification purposes, some baseline results are reported for this task on the official evaluation setups. Additional baseline results are also reported for Persian speech recognition. However, due to the space limitation in this paper, the baseline results are not reported for all the database parts and conditions. They will be defined and reported in the database technical documentation and in a future journal paper. Data Collection DeepMine is publicly available for everybody with a variety of licenses for different users. It was collected using crowdsourcing BIBREF4. The data collection was done using an Android application. Each respondent installed the application on his/her personal device and recorded several phrases in different sessions. The Android application did various checks on each utterance and if it passed all of them, the respondent was directed to the next phrase. For more information about data collection scenario, please refer to BIBREF4. Data Collection ::: Post-Processing In order to clean-up the database, the main post-processing step was to filter out problematic utterances. Possible problems include speaker word insertions (e.g. repeating some part of a phrase), deletions, substitutions, and involuntary disfluencies. To detect these, we implemented an alignment stage, similar to the second alignment stage in the LibriSpeech project BIBREF5. In this method, a custom decoding graph was generated for each phrase. The decoding graph allows for word skipping and word insertion in the phrase. For text-dependent and text-prompted parts of the database, such errors are not allowed. Hence, any utterances with errors were removed from the enrollment and test lists. For the speech recognition part, a sub-part of the utterance which is correctly aligned to the corresponding transcription is kept. After the cleaning step, around 190 thousand utterances with full transcription and 10 thousand with sub-part alignment have remained in the database. Data Collection ::: Statistics After processing the database and removing problematic respondents and utterances, 1969 respondents remained in the database, with 1149 of them being male and 820 female. 297 of the respondents could not read English and have therefore read only the Persian prompts. About 13200 sessions were recorded by females and similarly, about 9500 sessions by males, i.e. women are over-represented in terms of sessions, even though their number is 17% smaller than that of males. Other useful statistics related to the database are shown in Table TABREF4. The last status of the database, as well as other related and useful information about its availability can be found on its website, together with a limited number of samples. DeepMine Database Parts The DeepMine database consists of three parts. The first one contains fixed common phrases to perform text-dependent speaker verification. The second part consists of random sequences of words useful for text-prompted speaker verification, and the last part includes phrases with word- and phoneme-level transcription, useful for text-independent speaker verification using a random phrase (similar to Part4 of RedDots). This part can also serve for Persian ASR training. Each part is described in more details below. Table TABREF11 shows the number of unique phrases in each part of the database. For the English text-dependent part, the following phrases were selected from part1 of the RedDots database, hence the RedDots can be used as an additional training set for this part: “My voice is my password.” “OK Google.” “Artificial intelligence is for real.” “Actions speak louder than words.” “There is no such thing as a free lunch.” DeepMine Database Parts ::: Part1 - Text-dependent (TD) This part contains a set of fixed phrases which are used to verify speakers in text-dependent mode. Each speaker utters 5 Persian phrases, and if the speaker can read English, 5 phrases selected from Part1 of the RedDots database are also recorded. We have created three experimental setups with different numbers of speakers in the evaluation set. For each setup, speakers with more recording sessions are included in the evaluation set and the rest of the speakers are used for training in the background set (in the database, all background sets are basically training data). The rows in Table TABREF13 corresponds to the different experimental setups and shows the numbers of speakers in each set. Note that, for English, we have filtered the (Persian native) speakers by the ability to read English. Therefore, there are fewer speakers in each set for English than for Persian. There is a small “dev” set in each setup which can be used for parameter tuning to prevent over-tuning on the evaluation set. For each experimental setup, we have defined several official trial lists with different numbers of enrollment utterances per trial in order to investigate the effects of having different amounts of enrollment data. All trials in one trial list have the same number of enrollment utterances (3 to 6) and only one test utterance. All enrollment utterances in a trial are taken from different consecutive sessions and the test utterance is taken from yet another session. From all the setups and conditions, the 100-spk with 3-session enrollment (3-sess) is considered as the main evaluation condition. In Table TABREF14, the number of trials for Persian 3-sess are shown for the different types of trial in the text-dependent speaker verification (SV). Note that for Imposter-Wrong (IW) trials (i.e. imposter speaker pronouncing wrong phrase), we merely create one wrong trial for each Imposter-Correct (IC) trial to limit the huge number of possible trials for this case. So, the number of trials for IC and IW cases are the same. DeepMine Database Parts ::: Part2 - Text-prompted (TP) For this part, in each session, 3 random sequences of Persian month names are shown to the respondent in two modes: In the first mode, the sequence consists of all 12 months, which will be used for speaker enrollment. The second mode contains a sequence of 3 month names that will be used as a test utterance. In each 8 sessions received by a respondent from the server, there are 3 enrollment phrases of all 12 months (all in just one session), and $7 \times 3$ other test phrases, containing fewer words. For a respondent who can read English, 3 random sequences of English digits are also recorded in each session. In one of the sessions, these sequences contain all digits and the remaining ones contain only 4 digits. Similar to the text-dependent case, three experimental setups with different number of speaker in the evaluation set are defined (corresponding to the rows in Table TABREF16). However, different strategy is used for defining trials: Depending on the enrollment condition (1- to 3-sess), trials are enrolled on utterances of all words from 1 to 3 different sessions (i.e. 3 to 9 utterances). Further, we consider two conditions for test utterances: seq test utterance with only 3 or 4 words and full test utterances with all words (i.e. same words as in enrollment but in different order). From all setups an all conditions, the 100-spk with 1-session enrolment (1-sess) is considered as the main evaluation condition for the text-prompted case. In Table TABREF16, the numbers of trials (sum for both seq and full conditions) for Persian 1-sess are shown for the different types of trials in the text-prompted SV. Again, we just create one IW trial for each IC trial. DeepMine Database Parts ::: Part3 - Text-independent (TI) In this part, 8 Persian phrases that have already been transcribed on the phone level are displayed to the respondent. These phrases are chosen mostly from news and Persian Wikipedia. If the respondent is unable to read English, instead of 5 fixed phrases and 3 random digit strings, 8 other Persian phrases are also prompted to the respondent to have exactly 24 phrases in each recording session. This part can be useful at least for three potential applications. First, it can be used for text-independent speaker verification. The second application of this part (same as Part4 of RedDots) is text-prompted speaker verification using random text (instead of a random sequence of words). Finally, the third application is large vocabulary speech recognition in Persian (explained in the next sub-section). Based on the recording sessions, we created two experimental setups for speaker verification. In the first one, respondents with at least 17 recording sessions are included to the evaluation set, respondents with 16 sessions to the development and the rest of respondents to the background set (can be used as training data). In the second setup, respondents with at least 8 sessions are included to the evaluation set, respondents with 6 or 7 sessions to the development and the rest of respondents to the background set. Table TABREF18 shows numbers of speakers in each set of the database for text-independent SV case. For text-independent SV, we have considered 4 scenarios for enrollment and 4 scenarios for test. The speaker can be enrolled using utterances from 1, 2 or 3 consecutive sessions (1sess to 3sess) or using 8 utterances from 8 different sessions. The test speech can be one utterance (1utt) for short duration scenario or all utterances in one session (1sess) for long duration case. In addition, test speech can be selected from 5 English phrases for cross-language testing (enrollment using Persian utterances and test using English utterances). From all setups, 1sess-1utt and 1sess-1sess for 438-spk set are considered as the main evaluation setups for text-independent case. Table TABREF19 shows number of trials for these setups. For text-prompted SV with random text, the same setup as text-independent case together with corresponding utterance transcriptions can be used. DeepMine Database Parts ::: Part3 - Speech Recognition As explained before, Part3 of the DeepMine database can be used for Persian read speech recognition. There are only a few databases for speech recognition in Persian BIBREF6, BIBREF7. Hence, this part can at least partly address this problem and enable robust speech recognition applications in Persian. Additionally, it can be used for speaker recognition applications, such as training deep neural networks (DNNs) for extracting bottleneck features BIBREF8, or for collecting sufficient statistics using DNNs for i-vector training. We have randomly selected 50 speakers (25 for each gender) from the all speakers in the database which have net speech (without silence parts) between 25 minutes to 50 minutes as test speakers. For each speaker, the utterances in the first 5 sessions are included to (small) test-set and the other utterances of test speakers are considered as a large-test-set. The remaining utterances of the other speakers are included in the training set. The test-set, large-test-set and train-set contain 5.9, 28.5 and 450 hours of speech respectively. There are about 8300 utterances in Part3 which contain only Persian full names (i.e. first and family name pairs). Each phrase consists of several full names and their phoneme transcriptions were extracted automatically using a trained Grapheme-to-Phoneme (G2P). These utterances can be used to evaluate the performance of a systems for name recognition, which is usually more difficult than the normal speech recognition because of the lack of a reliable language model. Experiments and Results Due to the space limitation, we present results only for the Persian text-dependent speaker verification and speech recognition. Experiments and Results ::: Speaker Verification Experiments We conducted an experiment on text-dependent speaker verification part of the database, using the i-vector based method proposed in BIBREF9, BIBREF10 and applied it to the Persian portion of Part1. In this experiment, 20-dimensional MFCC features along with first and second derivatives are extracted from 16 kHz signals using HTK BIBREF11 with 25 ms Hamming windowed frames with 15 ms overlap. The reported results are obtained with a 400-dimensional gender independent i-vector based system. The i-vectors are first length-normalized and are further normalized using phrase- and gender-dependent Regularized Within-Class Covariance Normalization (RWCCN) BIBREF10. Cosine distance is used to obtain speaker verification scores and phrase- and gender-dependent s-norm is used for normalizing the scores. For aligning speech frames to Gaussian components, monophone HMMs with 3 states and 8 Gaussian components in each state are used BIBREF10. We only model the phonemes which appear in the 5 Persian text-dependent phrases. For speaker verification experiments, the results were reported in terms of Equal Error Rate (EER) and Normalized Detection Cost Function as defined for NIST SRE08 ($\mathrm {NDCF_{0.01}^{min}}$) and NIST SRE10 ($\mathrm {NDCF_{0.001}^{min}}$). As shown in Table TABREF22, in text-dependent SV there are 4 types of trials: Target-Correct and Imposter-Correct refer to trials when the pass-phrase is uttered correctly by target and imposter speakers respectively, and in same manner, Target-Wrong and Imposter-Wrong refer to trials when speakers uttered a wrong pass-phrase. In this paper, only the correct trials (i.e. Target-Correct as target trials vs Imposter-Correct as non-target trials) are considered for evaluating systems as it has been proved that these are the most challenging trials in text-dependent SV BIBREF8, BIBREF12. Table TABREF23 shows the results of text-dependent experiments using Persian 100-spk and 3-sess setup. For filtering trials, the respondents' mobile brand and model were used in this experiment. In the table, the first two letters in the filter notation relate to the target trials and the second two letters (i.e. right side of the colon) relate for non-target trials. For target trials, the first Y means the enrolment and test utterances were recorded using a device with the same brand by the target speaker. The second Y letter means both recordings were done using exactly the same device model. Similarly, the first Y for non-target trials means that the devices of target and imposter speakers are from the same brand (i.e. manufacturer). The second Y means that, in addition to the same brand, both devices have the same model. So, the most difficult target trials are “NN”, where the speaker has used different a device at the test time. In the same manner, the most difficult non-target trials which should be rejected by the system are “YY” where the imposter speaker has used the same device model as the target speaker (note that it does not mean physically the same device because each speaker participated in the project using a personal mobile device). Hence, the similarity in the recording channel makes rejection more difficult. The first row in Table TABREF23 shows the results for all trials. By comparing the results with the best published results on RSR2015 and RedDots BIBREF10, BIBREF8, BIBREF12, it is clear that the DeepMine database is more challenging than both RSR2015 and RedDots databases. For RSR2015, the same i-vector/HMM-based method with both RWCCN and s-norm has achieved EER less than 0.3% for both genders (Table VI in BIBREF10). The conventional Relevance MAP adaptation with HMM alignment without applying any channel-compensation techniques (i.e. without applying RWCCN and s-norm due to the lack of suitable training data) on RedDots Part1 for the male has achieved EER around 1.5% (Table XI in BIBREF10). It is worth noting that EERs for DeepMine database without any channel-compensation techniques are 2.1 and 3.7% for males and females respectively. One interesting advantage of the DeepMine database compared to both RSR2015 and RedDots is having several target speakers with more than one mobile device. This is allows us to analyse the effects of channel compensation methods. The second row in Table TABREF23 corresponds to the most difficult trials where the target trials come from mobile devices with different models while imposter trials come from the same device models. It is clear that severe degradation was caused by this kind of channel effects (i.e. decreasing within-speaker similarities while increasing between-speaker similarities), especially for females. The results in the third row show the condition when target speakers at the test time use exactly the same device that was used for enrollment. Comparing this row with the results in the first row proves how much improvement can be achieved when exactly the same device is used by the target speaker. The results in the fourth row show the condition when imposter speakers also use the same device model at test time to fool the system. So, in this case, there is no device mismatch in all trials. By comparing the results with the third row, we can see how much degradation is caused if we only consider the non-target trials with the same device. The fifth row shows similar results when the imposter speakers use device of the same brand as the target speaker but with a different model. Surprisingly, in this case, the degradation is negligible and it means that mobiles from a specific brand (manufacturer) have different recording channel properties. The degraded female results in the sixth row as compared to the third row show the effect of using a different device model from the same brand for target trials. For males, the filters brings almost the same subsets of trials, which explains the very similar results in this case. Looking at the first two and the last row of Table TABREF23, one can notice the significantly worse performance obtained for the female trials as compared to males. Note that these three rows include target trials where the devices used for enrollment do not necessarily match the devices used for recording test utterances. On the other hand, in rows 3 to 6, which exclude such mismatched trials, the performance for males and females is comparable. This suggest that the degraded results for females are caused by some problematic trials with device mismatch. The exact reason for this degradation is so far unclear and needs a further investigation. In the last row of the table, the condition of the second row is relaxed: the target device should have different model possibly from the same brand and imposter device only needs to be from the same brand. In this case, as was expected, the performance degradation is smaller than in the second row. Experiments and Results ::: Speech Recognition Experiments In addition to speaker verification, we present several speech recognition experiments on Part3. The experiments were performed with the Kaldi toolkit BIBREF13. For training HMM-based MonoPhone model, only 20 thousands of shortest utterances are used and for other models the whole training data is used. The DNN based acoustic model is a time-delay DNN with low-rank factorized layers and skip connections without i-vector adaptation (a modified network from one of the best performing LibriSpeech recipes). The network is shown in Table TABREF25: there are 16 F-TDNN layers, with dimension 1536 and linear bottleneck layers of dimension 256. The acoustic model is trained for 10 epochs using lattice-free maximum mutual information (LF-MMI) with cross-entropy regularization BIBREF14. Re-scoring is done using a pruned trigram language model and the size of the dictionary is around 90,000 words. Table TABREF26 shows the results in terms of word error rate (WER) for different evaluated methods. As can be seen, the created database can be used to train well performing and practically usable Persian ASR models. Conclusions In this paper, we have described the final version of a large speech corpus, the DeepMine database. It has been collected using crowdsourcing and, according to the best of our knowledge, it is the largest public text-dependent and text-prompted speaker verification database in two languages: Persian and English. In addition, it is the largest text-independent speaker verification evaluation database, making it suitable to robustly evaluate state-of-the-art methods on different conditions. Alongside these appealing properties, it comes with phone-level transcription, making it suitable to train deep neural network models for Persian speech recognition. We provided several evaluation protocols for each part of the database. The protocols allow researchers to investigate the performance of different methods in various scenarios and study the effects of channels, duration and phrase text on the performance. We also provide two test sets for speech recognition: One normal test set with a few minutes of speech for each speaker and one large test set with more (30 minutes on average) speech that can be used for any speaker adaptation method. As baseline results, we reported the performance of an i-vector/HMM based method on Persian text-dependent part. Moreover, we conducted speech recognition experiments using conventional HMM-based methods, as well as state-of-the-art deep neural network based method using Kaldi toolkit with promising performance. Text-dependent results have shown that the DeepMine database is more challenging than RSR2015 and RedDots databases. Acknowledgments The data collection project was mainly supported by Sharif DeepMine company. The work on the paper was supported by Czech National Science Foundation (GACR) project "NEUREM3" No. 19-26934X and the National Programme of Sustainability (NPU II) project "IT4Innovations excellence in science - LQ1602".
what age range is in the data?
d93c0e78a3fe890cd534a11276e934be68583f4b
what Ġage Ġrange Ġis Ġin Ġthe Ġdata ?
Un answer able
<s> what Ġage Ġrange Ġis Ġin Ġthe Ġdata ? </s> Introduction </s> Now adays Ġdeep Ġlearning Ġtechniques Ġoutper form Ġthe Ġother Ġconventional Ġmethods Ġin Ġmost Ġof Ġthe Ġspeech - related Ġtasks . ĠTraining Ġrobust Ġdeep Ġneural Ġnetworks Ġfor Ġeach Ġtask Ġdepends Ġon Ġthe Ġavailability Ġof Ġpowerful Ġprocessing ĠGPUs , Ġas Ġwell Ġas Ġstandard Ġand Ġlarge Ġscale Ġdatasets . ĠIn Ġtext - independent Ġspeaker Ġverification , Ġlarge - scale Ġdatasets Ġare Ġavailable , Ġthanks Ġto Ġthe ĠN IST ĠS RE Ġevaluations Ġand Ġother Ġdata Ġcollection Ġprojects Ġsuch Ġas ĠVox Cele b ĠB IB REF 0 . </s> In Ġtext - dependent Ġspeaker Ġrecognition , Ġexperiments Ġwith Ġend - to - end Ġarchitectures Ġconducted Ġon Ġlarge Ġproprietary Ġdatabases Ġhave Ġdemonstrated Ġtheir Ġsuperiority Ġover Ġtraditional Ġapproaches ĠB IB REF 1 . ĠYet , Ġcontrary Ġto Ġtext - independent Ġspeaker Ġrecognition , Ġtext - dependent Ġspeaker Ġrecognition Ġlacks Ġlarge - scale Ġpublicly Ġavailable Ġdatabases . ĠThe Ġtwo Ġmost Ġwell - known Ġdatasets Ġare Ġprobably ĠR SR 2015 ĠB IB REF 2 Ġand ĠRed D ots ĠB IB REF 3 . ĠThe Ġformer Ġcontains Ġspeech Ġdata Ġcollected Ġfrom Ġ300 Ġindividuals Ġin Ġa Ġcontrolled Ġmanner , Ġwhile Ġthe Ġlatter Ġis Ġused Ġprimarily Ġfor Ġevaluation Ġrather Ġthan Ġtraining , Ġdue Ġto Ġits Ġsmall Ġnumber Ġof Ġspeakers Ġ( only Ġ64 ). ĠMot ivated Ġby Ġthis Ġlack Ġof Ġlarge - scale Ġdataset Ġfor Ġtext - dependent Ġspeaker Ġverification , Ġwe Ġchose Ġto Ġproceed Ġwith Ġthe Ġcollection Ġof Ġthe ĠDeep Mine Ġdataset , Ġwhich Ġwe Ġexpect Ġto Ġbecome Ġa Ġstandard Ġbenchmark Ġfor Ġthe Ġtask . </s> Apart Ġfrom Ġspeaker Ġrecognition , Ġlarge Ġamounts Ġof Ġtraining Ġdata Ġare Ġrequired Ġalso Ġfor Ġtraining Ġautomatic Ġspeech Ġrecognition Ġ( AS R ) Ġsystems . ĠSuch Ġdatasets Ġshould Ġnot Ġonly Ġbe Ġlarge Ġin Ġsize , Ġthey Ġshould Ġalso Ġbe Ġcharacterized Ġby Ġhigh Ġvariability Ġwith Ġrespect Ġto Ġspeakers , Ġage Ġand Ġdialect s . ĠWhile Ġseveral Ġdatasets Ġwith Ġthese Ġproperties Ġare Ġavailable Ġfor Ġlanguages Ġlike ĠEnglish , ĠMandarin , ĠFrench , Ġthis Ġis Ġnot Ġthe Ġcase Ġfor Ġseveral Ġother Ġlanguages , Ġsuch Ġas ĠPersian . ĠTo Ġthis Ġend , Ġwe Ġproceeded Ġwith Ġcollecting Ġa Ġlarge - scale Ġdataset , Ġsuitable Ġfor Ġbuilding Ġrobust ĠAS R Ġmodels Ġin ĠPersian . </s> The Ġmain Ġgoal Ġof Ġthe ĠDeep Mine Ġproject Ġwas Ġto Ġcollect Ġspeech Ġfrom Ġat Ġleast Ġa Ġfew Ġthousand Ġspeakers , Ġenabling Ġresearch Ġand Ġdevelopment Ġof Ġdeep Ġlearning Ġmethods . ĠThe Ġproject Ġstarted Ġat Ġthe Ġbeginning Ġof Ġ2017 , Ġand Ġafter Ġdesigning Ġthe Ġdatabase Ġand Ġthe Ġdeveloping ĠAndroid Ġand Ġserver Ġapplications , Ġthe Ġdata Ġcollection Ġbegan Ġin Ġthe Ġmiddle Ġof Ġ2017 . ĠThe Ġproject Ġfinished Ġat Ġthe Ġend Ġof Ġ2018 Ġand Ġthe Ġcleaned - up Ġand Ġfinal Ġversion Ġof Ġthe Ġdatabase Ġwas Ġreleased Ġat Ġthe Ġbeginning Ġof Ġ2019 . ĠIn ĠB IB REF 4 , Ġthe Ġrunning Ġproject Ġand Ġits Ġdata Ġcollection Ġscenarios Ġwere Ġdescribed , Ġalongside Ġwith Ġsome Ġpreliminary Ġresults Ġand Ġstatistics . ĠIn Ġthis Ġpaper , Ġwe Ġannounce Ġthe Ġfinal Ġand Ġcleaned - up Ġversion Ġof Ġthe Ġdatabase , Ġdescribe Ġits Ġdifferent Ġparts Ġand Ġprovide Ġvarious Ġevaluation Ġsetups Ġfor Ġeach Ġpart . ĠFinally , Ġsince Ġthe Ġdatabase Ġwas Ġdesigned Ġmainly Ġfor Ġtext - dependent Ġspeaker Ġverification Ġpurposes , Ġsome Ġbaseline Ġresults Ġare Ġreported Ġfor Ġthis Ġtask Ġon Ġthe Ġofficial Ġevaluation Ġsetups . ĠAdditional Ġbaseline Ġresults Ġare Ġalso Ġreported Ġfor ĠPersian Ġspeech Ġrecognition . ĠHowever , Ġdue Ġto Ġthe Ġspace Ġlimitation Ġin Ġthis Ġpaper , Ġthe Ġbaseline Ġresults Ġare Ġnot Ġreported Ġfor Ġall Ġthe Ġdatabase Ġparts Ġand Ġconditions . ĠThey Ġwill Ġbe Ġdefined Ġand Ġreported Ġin Ġthe Ġdatabase Ġtechnical Ġdocumentation Ġand Ġin Ġa Ġfuture Ġjournal Ġpaper . </s> Data ĠCollection </s> Deep Mine Ġis Ġpublicly Ġavailable Ġfor Ġeverybody Ġwith Ġa Ġvariety Ġof Ġlicenses Ġfor Ġdifferent Ġusers . ĠIt Ġwas Ġcollected Ġusing Ġcrowds ourcing ĠB IB REF 4 . ĠThe Ġdata Ġcollection Ġwas Ġdone Ġusing Ġan ĠAndroid Ġapplication . ĠEach Ġrespondent Ġinstalled Ġthe Ġapplication Ġon Ġhis / her Ġpersonal Ġdevice Ġand Ġrecorded Ġseveral Ġphrases Ġin Ġdifferent Ġsessions . ĠThe ĠAndroid Ġapplication Ġdid Ġvarious Ġchecks Ġon Ġeach Ġutter ance Ġand Ġif Ġit Ġpassed Ġall Ġof Ġthem , Ġthe Ġrespondent Ġwas Ġdirected Ġto Ġthe Ġnext Ġphrase . ĠFor Ġmore Ġinformation Ġabout Ġdata Ġcollection Ġscenario , Ġplease Ġrefer Ġto ĠB IB REF 4 . </s> Data ĠCollection Ġ: :: ĠPost - Process ing </s> In Ġorder Ġto Ġclean - up Ġthe Ġdatabase , Ġthe Ġmain Ġpost - processing Ġstep Ġwas Ġto Ġfilter Ġout Ġproblematic Ġutter ances . ĠPossible Ġproblems Ġinclude Ġspeaker Ġword Ġinsert ions Ġ( e . g . Ġrepeating Ġsome Ġpart Ġof Ġa Ġphrase ), Ġdelet ions , Ġsubstit utions , Ġand Ġinvoluntary Ġdis flu encies . ĠTo Ġdetect Ġthese , Ġwe Ġimplemented Ġan Ġalignment Ġstage , Ġsimilar Ġto Ġthe Ġsecond Ġalignment Ġstage Ġin Ġthe ĠLib ri Spe ech Ġproject ĠB IB REF 5 . ĠIn Ġthis Ġmethod , Ġa Ġcustom Ġdecoding Ġgraph Ġwas Ġgenerated Ġfor Ġeach Ġphrase . ĠThe Ġdecoding Ġgraph Ġallows Ġfor Ġword Ġskipping Ġand Ġword Ġinsertion Ġin Ġthe Ġphrase . </s> For Ġtext - dependent Ġand Ġtext - prom pt ed Ġparts Ġof Ġthe Ġdatabase , Ġsuch Ġerrors Ġare Ġnot Ġallowed . ĠHence , Ġany Ġutter ances Ġwith Ġerrors Ġwere Ġremoved Ġfrom Ġthe Ġenrollment Ġand Ġtest Ġlists . ĠFor Ġthe Ġspeech Ġrecognition Ġpart , Ġa Ġsub - part Ġof Ġthe Ġutter ance Ġwhich Ġis Ġcorrectly Ġaligned Ġto Ġthe Ġcorresponding Ġtranscription Ġis Ġkept . ĠAfter Ġthe Ġcleaning Ġstep , Ġaround Ġ190 Ġthousand Ġutter ances Ġwith Ġfull Ġtranscription Ġand Ġ10 Ġthousand Ġwith Ġsub - part Ġalignment Ġhave Ġremained Ġin Ġthe Ġdatabase . </s> Data ĠCollection Ġ: :: ĠStatistics </s> After Ġprocessing Ġthe Ġdatabase Ġand Ġremoving Ġproblematic Ġrespondents Ġand Ġutter ances , Ġ1969 Ġrespondents Ġremained Ġin Ġthe Ġdatabase , Ġwith Ġ11 49 Ġof Ġthem Ġbeing Ġmale Ġand Ġ820 Ġfemale . Ġ297 Ġof Ġthe Ġrespondents Ġcould Ġnot Ġread ĠEnglish Ġand Ġhave Ġtherefore Ġread Ġonly Ġthe ĠPersian Ġprompts . ĠAbout Ġ13 200 Ġsessions Ġwere Ġrecorded Ġby Ġfemales Ġand Ġsimilarly , Ġabout Ġ9 500 Ġsessions Ġby Ġmales , Ġi . e . Ġwomen Ġare Ġover - represented Ġin Ġterms Ġof Ġsessions , Ġeven Ġthough Ġtheir Ġnumber Ġis Ġ17 % Ġsmaller Ġthan Ġthat Ġof Ġmales . ĠOther Ġuseful Ġstatistics Ġrelated Ġto Ġthe Ġdatabase Ġare Ġshown Ġin ĠTable ĠT AB REF 4 . </s> The Ġlast Ġstatus Ġof Ġthe Ġdatabase , Ġas Ġwell Ġas Ġother Ġrelated Ġand Ġuseful Ġinformation Ġabout Ġits Ġavailability Ġcan Ġbe Ġfound Ġon Ġits Ġwebsite , Ġtogether Ġwith Ġa Ġlimited Ġnumber Ġof Ġsamples . </s> Deep Mine ĠDatabase ĠParts </s> The ĠDeep Mine Ġdatabase Ġconsists Ġof Ġthree Ġparts . ĠThe Ġfirst Ġone Ġcontains Ġfixed Ġcommon Ġphrases Ġto Ġperform Ġtext - dependent Ġspeaker Ġverification . ĠThe Ġsecond Ġpart Ġconsists Ġof Ġrandom Ġsequences Ġof Ġwords Ġuseful Ġfor Ġtext - prom pt ed Ġspeaker Ġverification , Ġand Ġthe Ġlast Ġpart Ġincludes Ġphrases Ġwith Ġword - Ġand Ġphon eme - level Ġtranscription , Ġuseful Ġfor Ġtext - independent Ġspeaker Ġverification Ġusing Ġa Ġrandom Ġphrase Ġ( similar Ġto ĠPart 4 Ġof ĠRed D ots ). ĠThis Ġpart Ġcan Ġalso Ġserve Ġfor ĠPersian ĠAS R Ġtraining . ĠEach Ġpart Ġis Ġdescribed Ġin Ġmore Ġdetails Ġbelow . ĠTable ĠT AB REF 11 Ġshows Ġthe Ġnumber Ġof Ġunique Ġphrases Ġin Ġeach Ġpart Ġof Ġthe Ġdatabase . ĠFor Ġthe ĠEnglish Ġtext - dependent Ġpart , Ġthe Ġfollowing Ġphrases Ġwere Ġselected Ġfrom Ġpart 1 Ġof Ġthe ĠRed D ots Ġdatabase , Ġhence Ġthe ĠRed D ots Ġcan Ġbe Ġused Ġas Ġan Ġadditional Ġtraining Ġset Ġfor Ġthis Ġpart : </s> âĢ ľ My Ġvoice Ġis Ġmy Ġpassword . âĢ Ŀ </s> âĢ ľ OK ĠGoogle . âĢ Ŀ </s> âĢ ľ Art ificial Ġintelligence Ġis Ġfor Ġreal . âĢ Ŀ </s> âĢ ľ A ctions Ġspeak Ġlouder Ġthan Ġwords . âĢ Ŀ </s> âĢ ľ There Ġis Ġno Ġsuch Ġthing Ġas Ġa Ġfree Ġlunch . âĢ Ŀ </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 1 Ġ- ĠText - dependent Ġ( TD ) </s> This Ġpart Ġcontains Ġa Ġset Ġof Ġfixed Ġphrases Ġwhich Ġare Ġused Ġto Ġverify Ġspeakers Ġin Ġtext - dependent Ġmode . ĠEach Ġspeaker Ġut ters Ġ5 ĠPersian Ġphrases , Ġand Ġif Ġthe Ġspeaker Ġcan Ġread ĠEnglish , Ġ5 Ġphrases Ġselected Ġfrom ĠPart 1 Ġof Ġthe ĠRed D ots Ġdatabase Ġare Ġalso Ġrecorded . </s> We Ġhave Ġcreated Ġthree Ġexperimental Ġsetups Ġwith Ġdifferent Ġnumbers Ġof Ġspeakers Ġin Ġthe Ġevaluation Ġset . ĠFor Ġeach Ġsetup , Ġspeakers Ġwith Ġmore Ġrecording Ġsessions Ġare Ġincluded Ġin Ġthe Ġevaluation Ġset Ġand Ġthe Ġrest Ġof Ġthe Ġspeakers Ġare Ġused Ġfor Ġtraining Ġin Ġthe Ġbackground Ġset Ġ( in Ġthe Ġdatabase , Ġall Ġbackground Ġsets Ġare Ġbasically Ġtraining Ġdata ). ĠThe Ġrows Ġin ĠTable ĠT AB REF 13 Ġcorresponds Ġto Ġthe Ġdifferent Ġexperimental Ġsetups Ġand Ġshows Ġthe Ġnumbers Ġof Ġspeakers Ġin Ġeach Ġset . ĠNote Ġthat , Ġfor ĠEnglish , Ġwe Ġhave Ġfiltered Ġthe Ġ( Pers ian Ġnative ) Ġspeakers Ġby Ġthe Ġability Ġto Ġread ĠEnglish . ĠTherefore , Ġthere Ġare Ġfewer Ġspeakers Ġin Ġeach Ġset Ġfor ĠEnglish Ġthan Ġfor ĠPersian . ĠThere Ġis Ġa Ġsmall ĠâĢ ľ dev âĢ Ŀ Ġset Ġin Ġeach Ġsetup Ġwhich Ġcan Ġbe Ġused Ġfor Ġparameter Ġtuning Ġto Ġprevent Ġover - tun ing Ġon Ġthe Ġevaluation Ġset . </s> For Ġeach Ġexperimental Ġsetup , Ġwe Ġhave Ġdefined Ġseveral Ġofficial Ġtrial Ġlists Ġwith Ġdifferent Ġnumbers Ġof Ġenrollment Ġutter ances Ġper Ġtrial Ġin Ġorder Ġto Ġinvestigate Ġthe Ġeffects Ġof Ġhaving Ġdifferent Ġamounts Ġof Ġenrollment Ġdata . ĠAll Ġtrials Ġin Ġone Ġtrial Ġlist Ġhave Ġthe Ġsame Ġnumber Ġof Ġenrollment Ġutter ances Ġ( 3 Ġto Ġ6 ) Ġand Ġonly Ġone Ġtest Ġutter ance . ĠAll Ġenrollment Ġutter ances Ġin Ġa Ġtrial Ġare Ġtaken Ġfrom Ġdifferent Ġconsecutive Ġsessions Ġand Ġthe Ġtest Ġutter ance Ġis Ġtaken Ġfrom Ġyet Ġanother Ġsession . ĠFrom Ġall Ġthe Ġsetups Ġand Ġconditions , Ġthe Ġ100 - sp k Ġwith Ġ3 - session Ġenrollment Ġ( 3 - s ess ) Ġis Ġconsidered Ġas Ġthe Ġmain Ġevaluation Ġcondition . ĠIn ĠTable ĠT AB REF 14 , Ġthe Ġnumber Ġof Ġtrials Ġfor ĠPersian Ġ3 - s ess Ġare Ġshown Ġfor Ġthe Ġdifferent Ġtypes Ġof Ġtrial Ġin Ġthe Ġtext - dependent Ġspeaker Ġverification Ġ( S V ). ĠNote Ġthat Ġfor ĠImp oster - Wr ong Ġ( I W ) Ġtrials Ġ( i . e . Ġimp oster Ġspeaker Ġpron ouncing Ġwrong Ġphrase ), Ġwe Ġmerely Ġcreate Ġone Ġwrong Ġtrial Ġfor Ġeach ĠImp oster - Correct Ġ( IC ) Ġtrial Ġto Ġlimit Ġthe Ġhuge Ġnumber Ġof Ġpossible Ġtrials Ġfor Ġthis Ġcase . ĠSo , Ġthe Ġnumber Ġof Ġtrials Ġfor ĠIC Ġand ĠI W Ġcases Ġare Ġthe Ġsame . </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 2 Ġ- ĠText - prom pt ed Ġ( TP ) </s> For Ġthis Ġpart , Ġin Ġeach Ġsession , Ġ3 Ġrandom Ġsequences Ġof ĠPersian Ġmonth Ġnames Ġare Ġshown Ġto Ġthe Ġrespondent Ġin Ġtwo Ġmodes : ĠIn Ġthe Ġfirst Ġmode , Ġthe Ġsequence Ġconsists Ġof Ġall Ġ12 Ġmonths , Ġwhich Ġwill Ġbe Ġused Ġfor Ġspeaker Ġenrollment . ĠThe Ġsecond Ġmode Ġcontains Ġa Ġsequence Ġof Ġ3 Ġmonth Ġnames Ġthat Ġwill Ġbe Ġused Ġas Ġa Ġtest Ġutter ance . ĠIn Ġeach Ġ8 Ġsessions Ġreceived Ġby Ġa Ġrespondent Ġfrom Ġthe Ġserver , Ġthere Ġare Ġ3 Ġenrollment Ġphrases Ġof Ġall Ġ12 Ġmonths Ġ( all Ġin Ġjust Ġone Ġsession ), Ġand Ġ$ 7 Ġ\ times Ġ3 $ Ġother Ġtest Ġphrases , Ġcontaining Ġfewer Ġwords . ĠFor Ġa Ġrespondent Ġwho Ġcan Ġread ĠEnglish , Ġ3 Ġrandom Ġsequences Ġof ĠEnglish Ġdigits Ġare Ġalso Ġrecorded Ġin Ġeach Ġsession . ĠIn Ġone Ġof Ġthe Ġsessions , Ġthese Ġsequences Ġcontain Ġall Ġdigits Ġand Ġthe Ġremaining Ġones Ġcontain Ġonly Ġ4 Ġdigits . </s> Similar Ġto Ġthe Ġtext - dependent Ġcase , Ġthree Ġexperimental Ġsetups Ġwith Ġdifferent Ġnumber Ġof Ġspeaker Ġin Ġthe Ġevaluation Ġset Ġare Ġdefined Ġ( cor respond ing Ġto Ġthe Ġrows Ġin ĠTable ĠT AB REF 16 ). ĠHowever , Ġdifferent Ġstrategy Ġis Ġused Ġfor Ġdefining Ġtrials : ĠDepending Ġon Ġthe Ġenrollment Ġcondition Ġ( 1 - Ġto Ġ3 - s ess ), Ġtrials Ġare Ġenrolled Ġon Ġutter ances Ġof Ġall Ġwords Ġfrom Ġ1 Ġto Ġ3 Ġdifferent Ġsessions Ġ( i . e . Ġ3 Ġto Ġ9 Ġutter ances ). ĠFurther , Ġwe Ġconsider Ġtwo Ġconditions Ġfor Ġtest Ġutter ances : Ġseq Ġtest Ġutter ance Ġwith Ġonly Ġ3 Ġor Ġ4 Ġwords Ġand Ġfull Ġtest Ġutter ances Ġwith Ġall Ġwords Ġ( i . e . Ġsame Ġwords Ġas Ġin Ġenrollment Ġbut Ġin Ġdifferent Ġorder ). ĠFrom Ġall Ġsetups Ġan Ġall Ġconditions , Ġthe Ġ100 - sp k Ġwith Ġ1 - session Ġenrol ment Ġ( 1 - s ess ) Ġis Ġconsidered Ġas Ġthe Ġmain Ġevaluation Ġcondition Ġfor Ġthe Ġtext - prom pt ed Ġcase . ĠIn ĠTable ĠT AB REF 16 , Ġthe Ġnumbers Ġof Ġtrials Ġ( sum Ġfor Ġboth Ġseq Ġand Ġfull Ġconditions ) Ġfor ĠPersian Ġ1 - s ess Ġare Ġshown Ġfor Ġthe Ġdifferent Ġtypes Ġof Ġtrials Ġin Ġthe Ġtext - prom pt ed ĠSV . ĠAgain , Ġwe Ġjust Ġcreate Ġone ĠI W Ġtrial Ġfor Ġeach ĠIC Ġtrial . </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 3 Ġ- ĠText - independent Ġ( TI ) </s> In Ġthis Ġpart , Ġ8 ĠPersian Ġphrases Ġthat Ġhave Ġalready Ġbeen Ġtransc ribed Ġon Ġthe Ġphone Ġlevel Ġare Ġdisplayed Ġto Ġthe Ġrespondent . ĠThese Ġphrases Ġare Ġchosen Ġmostly Ġfrom Ġnews Ġand ĠPersian ĠWikipedia . ĠIf Ġthe Ġrespondent Ġis Ġunable Ġto Ġread ĠEnglish , Ġinstead Ġof Ġ5 Ġfixed Ġphrases Ġand Ġ3 Ġrandom Ġdigit Ġstrings , Ġ8 Ġother ĠPersian Ġphrases Ġare Ġalso Ġprompted Ġto Ġthe Ġrespondent Ġto Ġhave Ġexactly Ġ24 Ġphrases Ġin Ġeach Ġrecording Ġsession . </s> This Ġpart Ġcan Ġbe Ġuseful Ġat Ġleast Ġfor Ġthree Ġpotential Ġapplications . ĠFirst , Ġit Ġcan Ġbe Ġused Ġfor Ġtext - independent Ġspeaker Ġverification . ĠThe Ġsecond Ġapplication Ġof Ġthis Ġpart Ġ( same Ġas ĠPart 4 Ġof ĠRed D ots ) Ġis Ġtext - prom pt ed Ġspeaker Ġverification Ġusing Ġrandom Ġtext Ġ( instead Ġof Ġa Ġrandom Ġsequence Ġof Ġwords ). ĠFinally , Ġthe Ġthird Ġapplication Ġis Ġlarge Ġvocabulary Ġspeech Ġrecognition Ġin ĠPersian Ġ( expl ained Ġin Ġthe Ġnext Ġsub - section ). </s> Based Ġon Ġthe Ġrecording Ġsessions , Ġwe Ġcreated Ġtwo Ġexperimental Ġsetups Ġfor Ġspeaker Ġverification . ĠIn Ġthe Ġfirst Ġone , Ġrespondents Ġwith Ġat Ġleast Ġ17 Ġrecording Ġsessions Ġare Ġincluded Ġto Ġthe Ġevaluation Ġset , Ġrespondents Ġwith Ġ16 Ġsessions Ġto Ġthe Ġdevelopment Ġand Ġthe Ġrest Ġof Ġrespondents Ġto Ġthe Ġbackground Ġset Ġ( can Ġbe Ġused Ġas Ġtraining Ġdata ). ĠIn Ġthe Ġsecond Ġsetup , Ġrespondents Ġwith Ġat Ġleast Ġ8 Ġsessions Ġare Ġincluded Ġto Ġthe Ġevaluation Ġset , Ġrespondents Ġwith Ġ6 Ġor Ġ7 Ġsessions Ġto Ġthe Ġdevelopment Ġand Ġthe Ġrest Ġof Ġrespondents Ġto Ġthe Ġbackground Ġset . ĠTable ĠT AB REF 18 Ġshows Ġnumbers Ġof Ġspeakers Ġin Ġeach Ġset Ġof Ġthe Ġdatabase Ġfor Ġtext - independent ĠSV Ġcase . </s> For Ġtext - independent ĠSV , Ġwe Ġhave Ġconsidered Ġ4 Ġscenarios Ġfor Ġenrollment Ġand Ġ4 Ġscenarios Ġfor Ġtest . ĠThe Ġspeaker Ġcan Ġbe Ġenrolled Ġusing Ġutter ances Ġfrom Ġ1 , Ġ2 Ġor Ġ3 Ġconsecutive Ġsessions Ġ( 1 s ess Ġto Ġ3 s ess ) Ġor Ġusing Ġ8 Ġutter ances Ġfrom Ġ8 Ġdifferent Ġsessions . ĠThe Ġtest Ġspeech Ġcan Ġbe Ġone Ġutter ance Ġ( 1 utt ) Ġfor Ġshort Ġduration Ġscenario Ġor Ġall Ġutter ances Ġin Ġone Ġsession Ġ( 1 s ess ) Ġfor Ġlong Ġduration Ġcase . ĠIn Ġaddition , Ġtest Ġspeech Ġcan Ġbe Ġselected Ġfrom Ġ5 ĠEnglish Ġphrases Ġfor Ġcross - language Ġtesting Ġ( en rollment Ġusing ĠPersian Ġutter ances Ġand Ġtest Ġusing ĠEnglish Ġutter ances ). ĠFrom Ġall Ġsetups , Ġ1 s ess - 1 utt Ġand Ġ1 s ess - 1 s ess Ġfor Ġ4 38 - sp k Ġset Ġare Ġconsidered Ġas Ġthe Ġmain Ġevaluation Ġsetups Ġfor Ġtext - independent Ġcase . ĠTable ĠT AB REF 19 Ġshows Ġnumber Ġof Ġtrials Ġfor Ġthese Ġsetups . </s> For Ġtext - prom pt ed ĠSV Ġwith Ġrandom Ġtext , Ġthe Ġsame Ġsetup Ġas Ġtext - independent Ġcase Ġtogether Ġwith Ġcorresponding Ġutter ance Ġtranscript ions Ġcan Ġbe Ġused . </s> Deep Mine ĠDatabase ĠParts Ġ: :: ĠPart 3 Ġ- ĠSpeech ĠRecogn ition </s> As Ġexplained Ġbefore , ĠPart 3 Ġof Ġthe ĠDeep Mine Ġdatabase Ġcan Ġbe Ġused Ġfor ĠPersian Ġread Ġspeech Ġrecognition . ĠThere Ġare Ġonly Ġa Ġfew Ġdatabases Ġfor Ġspeech Ġrecognition Ġin ĠPersian ĠB IB REF 6 , ĠB IB REF 7 . ĠHence , Ġthis Ġpart Ġcan Ġat Ġleast Ġpartly Ġaddress Ġthis Ġproblem Ġand Ġenable Ġrobust Ġspeech Ġrecognition Ġapplications Ġin ĠPersian . ĠAdditionally , Ġit Ġcan Ġbe Ġused Ġfor Ġspeaker Ġrecognition Ġapplications , Ġsuch Ġas Ġtraining Ġdeep Ġneural Ġnetworks Ġ( D NN s ) Ġfor Ġextracting Ġbottleneck Ġfeatures ĠB IB REF 8 , Ġor Ġfor Ġcollecting Ġsufficient Ġstatistics Ġusing ĠD NN s Ġfor Ġi - vector Ġtraining . </s> We Ġhave Ġrandomly Ġselected Ġ50 Ġspeakers Ġ( 25 Ġfor Ġeach Ġgender ) Ġfrom Ġthe Ġall Ġspeakers Ġin Ġthe Ġdatabase Ġwhich Ġhave Ġnet Ġspeech Ġ( without Ġsilence Ġparts ) Ġbetween Ġ25 Ġminutes Ġto Ġ50 Ġminutes Ġas Ġtest Ġspeakers . ĠFor Ġeach Ġspeaker , Ġthe Ġutter ances Ġin Ġthe Ġfirst Ġ5 Ġsessions Ġare Ġincluded Ġto Ġ( small ) Ġtest - set Ġand Ġthe Ġother Ġutter ances Ġof Ġtest Ġspeakers Ġare Ġconsidered Ġas Ġa Ġlarge - test - set . ĠThe Ġremaining Ġutter ances Ġof Ġthe Ġother Ġspeakers Ġare Ġincluded Ġin Ġthe Ġtraining Ġset . ĠThe Ġtest - set , Ġlarge - test - set Ġand Ġtrain - set Ġcontain Ġ5 . 9 , Ġ28 . 5 Ġand Ġ450 Ġhours Ġof Ġspeech Ġrespectively . </s> There Ġare Ġabout Ġ8 300 Ġutter ances Ġin ĠPart 3 Ġwhich Ġcontain Ġonly ĠPersian Ġfull Ġnames Ġ( i . e . Ġfirst Ġand Ġfamily Ġname Ġpairs ). ĠEach Ġphrase Ġconsists Ġof Ġseveral Ġfull Ġnames Ġand Ġtheir Ġphon eme Ġtranscript ions Ġwere Ġextracted Ġautomatically Ġusing Ġa Ġtrained ĠG rap he me - to - Ph on eme Ġ( G 2 P ). ĠThese Ġutter ances Ġcan Ġbe Ġused Ġto Ġevaluate Ġthe Ġperformance Ġof Ġa Ġsystems Ġfor Ġname Ġrecognition , Ġwhich Ġis Ġusually Ġmore Ġdifficult Ġthan Ġthe Ġnormal Ġspeech Ġrecognition Ġbecause Ġof Ġthe Ġlack Ġof Ġa Ġreliable Ġlanguage Ġmodel . </s> Exper iments Ġand ĠResults </s> Due Ġto Ġthe Ġspace Ġlimitation , Ġwe Ġpresent Ġresults Ġonly Ġfor Ġthe ĠPersian Ġtext - dependent Ġspeaker Ġverification Ġand Ġspeech Ġrecognition . </s> Exper iments Ġand ĠResults Ġ: :: ĠSpeaker ĠVer ification ĠExper iments </s> We Ġconducted Ġan Ġexperiment Ġon Ġtext - dependent Ġspeaker Ġverification Ġpart Ġof Ġthe Ġdatabase , Ġusing Ġthe Ġi - vector Ġbased Ġmethod Ġproposed Ġin ĠB IB REF 9 , ĠB IB REF 10 Ġand Ġapplied Ġit Ġto Ġthe ĠPersian Ġportion Ġof ĠPart 1 . ĠIn Ġthis Ġexperiment , Ġ20 - dimensional ĠMF CC Ġfeatures Ġalong Ġwith Ġfirst Ġand Ġsecond Ġderivatives Ġare Ġextracted Ġfrom Ġ16 ĠkHz Ġsignals Ġusing ĠHT K ĠB IB REF 11 Ġwith Ġ25 Ġms ĠHam ming Ġwindow ed Ġframes Ġwith Ġ15 Ġms Ġoverlap . </s> The Ġreported Ġresults Ġare Ġobtained Ġwith Ġa Ġ400 - dimensional Ġgender Ġindependent Ġi - vector Ġbased Ġsystem . ĠThe Ġi - ve ctors Ġare Ġfirst Ġlength - normal ized Ġand Ġare Ġfurther Ġnormalized Ġusing Ġphrase - Ġand Ġgender - dependent ĠRegular ized ĠWithin - Class ĠCov ari ance ĠNormal ization Ġ( RW CC N ) ĠB IB REF 10 . ĠCos ine Ġdistance Ġis Ġused Ġto Ġobtain Ġspeaker Ġverification Ġscores Ġand Ġphrase - Ġand Ġgender - dependent Ġs - norm Ġis Ġused Ġfor Ġnormal izing Ġthe Ġscores . ĠFor Ġalign ing Ġspeech Ġframes Ġto ĠGa ussian Ġcomponents , Ġmon ophone ĠHM Ms Ġwith Ġ3 Ġstates Ġand Ġ8 ĠGa ussian Ġcomponents Ġin Ġeach Ġstate Ġare Ġused ĠB IB REF 10 . ĠWe Ġonly Ġmodel Ġthe Ġphon em es Ġwhich Ġappear Ġin Ġthe Ġ5 ĠPersian Ġtext - dependent Ġphrases . </s> For Ġspeaker Ġverification Ġexperiments , Ġthe Ġresults Ġwere Ġreported Ġin Ġterms Ġof ĠEqual ĠError ĠRate Ġ( E ER ) Ġand ĠNormal ized ĠDetection ĠCost ĠFunction Ġas Ġdefined Ġfor ĠN IST ĠS RE 08 Ġ($ \ math rm Ġ{ ND CF _{ 0 . 01 } ^{ min }} $ ) Ġand ĠN IST ĠS RE 10 Ġ($ \ math rm Ġ{ ND CF _{ 0 . 001 } ^{ min }} $ ). ĠAs Ġshown Ġin ĠTable ĠT AB REF 22 , Ġin Ġtext - dependent ĠSV Ġthere Ġare Ġ4 Ġtypes Ġof Ġtrials : ĠTarget - Correct Ġand ĠImp oster - Correct Ġrefer Ġto Ġtrials Ġwhen Ġthe Ġpass - phrase Ġis Ġuttered Ġcorrectly Ġby Ġtarget Ġand Ġimp oster Ġspeakers Ġrespectively , Ġand Ġin Ġsame Ġmanner , ĠTarget - Wr ong Ġand ĠImp oster - Wr ong Ġrefer Ġto Ġtrials Ġwhen Ġspeakers Ġuttered Ġa Ġwrong Ġpass - phrase . ĠIn Ġthis Ġpaper , Ġonly Ġthe Ġcorrect Ġtrials Ġ( i . e . ĠTarget - Correct Ġas Ġtarget Ġtrials Ġvs ĠImp oster - Correct Ġas Ġnon - target Ġtrials ) Ġare Ġconsidered Ġfor Ġevaluating Ġsystems Ġas Ġit Ġhas Ġbeen Ġproved Ġthat Ġthese Ġare Ġthe Ġmost Ġchallenging Ġtrials Ġin Ġtext - dependent ĠSV ĠB IB REF 8 , ĠB IB REF 12 . </s> Table ĠT AB REF 23 Ġshows Ġthe Ġresults Ġof Ġtext - dependent Ġexperiments Ġusing ĠPersian Ġ100 - sp k Ġand Ġ3 - s ess Ġsetup . ĠFor Ġfiltering Ġtrials , Ġthe Ġrespondents ' Ġmobile Ġbrand Ġand Ġmodel Ġwere Ġused Ġin Ġthis Ġexperiment . ĠIn Ġthe Ġtable , Ġthe Ġfirst Ġtwo Ġletters Ġin Ġthe Ġfilter Ġnotation Ġrelate Ġto Ġthe Ġtarget Ġtrials Ġand Ġthe Ġsecond Ġtwo Ġletters Ġ( i . e . Ġright Ġside Ġof Ġthe Ġcolon ) Ġrelate Ġfor Ġnon - target Ġtrials . ĠFor Ġtarget Ġtrials , Ġthe Ġfirst ĠY Ġmeans Ġthe Ġenrol ment Ġand Ġtest Ġutter ances Ġwere Ġrecorded Ġusing Ġa Ġdevice Ġwith Ġthe Ġsame Ġbrand Ġby Ġthe Ġtarget Ġspeaker . ĠThe Ġsecond ĠY Ġletter Ġmeans Ġboth Ġrecordings Ġwere Ġdone Ġusing Ġexactly Ġthe Ġsame Ġdevice Ġmodel . ĠSimilarly , Ġthe Ġfirst ĠY Ġfor Ġnon - target Ġtrials Ġmeans Ġthat Ġthe Ġdevices Ġof Ġtarget Ġand Ġimp oster Ġspeakers Ġare Ġfrom Ġthe Ġsame Ġbrand Ġ( i . e . Ġmanufacturer ). ĠThe Ġsecond ĠY Ġmeans Ġthat , Ġin Ġaddition Ġto Ġthe Ġsame Ġbrand , Ġboth Ġdevices Ġhave Ġthe Ġsame Ġmodel . ĠSo , Ġthe Ġmost Ġdifficult Ġtarget Ġtrials Ġare ĠâĢ ľ NN âĢ Ŀ , Ġwhere Ġthe Ġspeaker Ġhas Ġused Ġdifferent Ġa Ġdevice Ġat Ġthe Ġtest Ġtime . ĠIn Ġthe Ġsame Ġmanner , Ġthe Ġmost Ġdifficult Ġnon - target Ġtrials Ġwhich Ġshould Ġbe Ġrejected Ġby Ġthe Ġsystem Ġare ĠâĢ ľ YY âĢ Ŀ Ġwhere Ġthe Ġimp oster Ġspeaker Ġhas Ġused Ġthe Ġsame Ġdevice Ġmodel Ġas Ġthe Ġtarget Ġspeaker Ġ( note Ġthat Ġit Ġdoes Ġnot Ġmean Ġphysically Ġthe Ġsame Ġdevice Ġbecause Ġeach Ġspeaker Ġparticipated Ġin Ġthe Ġproject Ġusing Ġa Ġpersonal Ġmobile Ġdevice ). ĠHence , Ġthe Ġsimilarity Ġin Ġthe Ġrecording Ġchannel Ġmakes Ġrejection Ġmore Ġdifficult . </s> The Ġfirst Ġrow Ġin ĠTable ĠT AB REF 23 Ġshows Ġthe Ġresults Ġfor Ġall Ġtrials . ĠBy Ġcomparing Ġthe Ġresults Ġwith Ġthe Ġbest Ġpublished Ġresults Ġon ĠR SR 2015 Ġand ĠRed D ots ĠB IB REF 10 , ĠB IB REF 8 , ĠB IB REF 12 , Ġit Ġis Ġclear Ġthat Ġthe ĠDeep Mine Ġdatabase Ġis Ġmore Ġchallenging Ġthan Ġboth ĠR SR 2015 Ġand ĠRed D ots Ġdatabases . ĠFor ĠR SR 2015 , Ġthe Ġsame Ġi - vector / H MM - based Ġmethod Ġwith Ġboth ĠRW CC N Ġand Ġs - norm Ġhas Ġachieved ĠE ER Ġless Ġthan Ġ0 . 3 % Ġfor Ġboth Ġgenders Ġ( Table ĠVI Ġin ĠB IB REF 10 ). ĠThe Ġconventional ĠRe lev ance ĠMAP Ġadaptation Ġwith ĠH MM Ġalignment Ġwithout Ġapplying Ġany Ġchannel - comp ensation Ġtechniques Ġ( i . e . Ġwithout Ġapplying ĠRW CC N Ġand Ġs - norm Ġdue Ġto Ġthe Ġlack Ġof Ġsuitable Ġtraining Ġdata ) Ġon ĠRed D ots ĠPart 1 Ġfor Ġthe Ġmale Ġhas Ġachieved ĠE ER Ġaround Ġ1 . 5 % Ġ( Table ĠXI Ġin ĠB IB REF 10 ). ĠIt Ġis Ġworth Ġnoting Ġthat ĠE ER s Ġfor ĠDeep Mine Ġdatabase Ġwithout Ġany Ġchannel - comp ensation Ġtechniques Ġare Ġ2 . 1 Ġand Ġ3 . 7 % Ġfor Ġmales Ġand Ġfemales Ġrespectively . </s> One Ġinteresting Ġadvantage Ġof Ġthe ĠDeep Mine Ġdatabase Ġcompared Ġto Ġboth ĠR SR 2015 Ġand ĠRed D ots Ġis Ġhaving Ġseveral Ġtarget Ġspeakers Ġwith Ġmore Ġthan Ġone Ġmobile Ġdevice . ĠThis Ġis Ġallows Ġus Ġto Ġanalyse Ġthe Ġeffects Ġof Ġchannel Ġcompensation Ġmethods . ĠThe Ġsecond Ġrow Ġin ĠTable ĠT AB REF 23 Ġcorresponds Ġto Ġthe Ġmost Ġdifficult Ġtrials Ġwhere Ġthe Ġtarget Ġtrials Ġcome Ġfrom Ġmobile Ġdevices Ġwith Ġdifferent Ġmodels Ġwhile Ġimp oster Ġtrials Ġcome Ġfrom Ġthe Ġsame Ġdevice Ġmodels . ĠIt Ġis Ġclear Ġthat Ġsevere Ġdegradation Ġwas Ġcaused Ġby Ġthis Ġkind Ġof Ġchannel Ġeffects Ġ( i . e . Ġdecreasing Ġwithin - spe aker Ġsimilarities Ġwhile Ġincreasing Ġbetween - spe aker Ġsimilarities ), Ġespecially Ġfor Ġfemales . </s> The Ġresults Ġin Ġthe Ġthird Ġrow Ġshow Ġthe Ġcondition Ġwhen Ġtarget Ġspeakers Ġat Ġthe Ġtest Ġtime Ġuse Ġexactly Ġthe Ġsame Ġdevice Ġthat Ġwas Ġused Ġfor Ġenrollment . ĠComp aring Ġthis Ġrow Ġwith Ġthe Ġresults Ġin Ġthe Ġfirst Ġrow Ġproves Ġhow Ġmuch Ġimprovement Ġcan Ġbe Ġachieved Ġwhen Ġexactly Ġthe Ġsame Ġdevice Ġis Ġused Ġby Ġthe Ġtarget Ġspeaker . </s> The Ġresults Ġin Ġthe Ġfourth Ġrow Ġshow Ġthe Ġcondition Ġwhen Ġimp oster Ġspeakers Ġalso Ġuse Ġthe Ġsame Ġdevice Ġmodel Ġat Ġtest Ġtime Ġto Ġfool Ġthe Ġsystem . ĠSo , Ġin Ġthis Ġcase , Ġthere Ġis Ġno Ġdevice Ġmismatch Ġin Ġall Ġtrials . ĠBy Ġcomparing Ġthe Ġresults Ġwith Ġthe Ġthird Ġrow , Ġwe Ġcan Ġsee Ġhow Ġmuch Ġdegradation Ġis Ġcaused Ġif Ġwe Ġonly Ġconsider Ġthe Ġnon - target Ġtrials Ġwith Ġthe Ġsame Ġdevice . </s> The Ġfifth Ġrow Ġshows Ġsimilar Ġresults Ġwhen Ġthe Ġimp oster Ġspeakers Ġuse Ġdevice Ġof Ġthe Ġsame Ġbrand Ġas Ġthe Ġtarget Ġspeaker Ġbut Ġwith Ġa Ġdifferent Ġmodel . ĠSurprisingly , Ġin Ġthis Ġcase , Ġthe Ġdegradation Ġis Ġnegligible Ġand Ġit Ġmeans Ġthat Ġmob iles Ġfrom Ġa Ġspecific Ġbrand Ġ( manufact urer ) Ġhave Ġdifferent Ġrecording Ġchannel Ġproperties . </s> The Ġdegraded Ġfemale Ġresults Ġin Ġthe Ġsixth Ġrow Ġas Ġcompared Ġto Ġthe Ġthird Ġrow Ġshow Ġthe Ġeffect Ġof Ġusing Ġa Ġdifferent Ġdevice Ġmodel Ġfrom Ġthe Ġsame Ġbrand Ġfor Ġtarget Ġtrials . ĠFor Ġmales , Ġthe Ġfilters Ġbrings Ġalmost Ġthe Ġsame Ġsubs ets Ġof Ġtrials , Ġwhich Ġexplains Ġthe Ġvery Ġsimilar Ġresults Ġin Ġthis Ġcase . </s> Looking Ġat Ġthe Ġfirst Ġtwo Ġand Ġthe Ġlast Ġrow Ġof ĠTable ĠT AB REF 23 , Ġone Ġcan Ġnotice Ġthe Ġsignificantly Ġworse Ġperformance Ġobtained Ġfor Ġthe Ġfemale Ġtrials Ġas Ġcompared Ġto Ġmales . ĠNote Ġthat Ġthese Ġthree Ġrows Ġinclude Ġtarget Ġtrials Ġwhere Ġthe Ġdevices Ġused Ġfor Ġenrollment Ġdo Ġnot Ġnecessarily Ġmatch Ġthe Ġdevices Ġused Ġfor Ġrecording Ġtest Ġutter ances . ĠOn Ġthe Ġother Ġhand , Ġin Ġrows Ġ3 Ġto Ġ6 , Ġwhich Ġexclude Ġsuch Ġmism atched Ġtrials , Ġthe Ġperformance Ġfor Ġmales Ġand Ġfemales Ġis Ġcomparable . ĠThis Ġsuggest Ġthat Ġthe Ġdegraded Ġresults Ġfor Ġfemales Ġare Ġcaused Ġby Ġsome Ġproblematic Ġtrials Ġwith Ġdevice Ġmismatch . ĠThe Ġexact Ġreason Ġfor Ġthis Ġdegradation Ġis Ġso Ġfar Ġunclear Ġand Ġneeds Ġa Ġfurther Ġinvestigation . </s> In Ġthe Ġlast Ġrow Ġof Ġthe Ġtable , Ġthe Ġcondition Ġof Ġthe Ġsecond Ġrow Ġis Ġrelaxed : Ġthe Ġtarget Ġdevice Ġshould Ġhave Ġdifferent Ġmodel Ġpossibly Ġfrom Ġthe Ġsame Ġbrand Ġand Ġimp oster Ġdevice Ġonly Ġneeds Ġto Ġbe Ġfrom Ġthe Ġsame Ġbrand . ĠIn Ġthis Ġcase , Ġas Ġwas Ġexpected , Ġthe Ġperformance Ġdegradation Ġis Ġsmaller Ġthan Ġin Ġthe Ġsecond Ġrow . </s> Exper iments Ġand ĠResults Ġ: :: ĠSpeech ĠRecogn ition ĠExper iments </s> In Ġaddition Ġto Ġspeaker Ġverification , Ġwe Ġpresent Ġseveral Ġspeech Ġrecognition Ġexperiments Ġon ĠPart 3 . ĠThe Ġexperiments Ġwere Ġperformed Ġwith Ġthe ĠK aldi Ġtool kit ĠB IB REF 13 . ĠFor Ġtraining ĠH MM - based ĠMono Phone Ġmodel , Ġonly Ġ20 Ġthousands Ġof Ġshortest Ġutter ances Ġare Ġused Ġand Ġfor Ġother Ġmodels Ġthe Ġwhole Ġtraining Ġdata Ġis Ġused . ĠThe ĠD NN Ġbased Ġacoustic Ġmodel Ġis Ġa Ġtime - delay ĠD NN Ġwith Ġlow - rank Ġfactor ized Ġlayers Ġand Ġskip Ġconnections Ġwithout Ġi - vector Ġadaptation Ġ( a Ġmodified Ġnetwork Ġfrom Ġone Ġof Ġthe Ġbest Ġperforming ĠLib ri Spe ech Ġrecipes ). ĠThe Ġnetwork Ġis Ġshown Ġin ĠTable ĠT AB REF 25 : Ġthere Ġare Ġ16 ĠF - TD NN Ġlayers , Ġwith Ġdimension Ġ15 36 Ġand Ġlinear Ġbottleneck Ġlayers Ġof Ġdimension Ġ256 . ĠThe Ġacoustic Ġmodel Ġis Ġtrained Ġfor Ġ10 Ġepoch s Ġusing Ġlatt ice - free Ġmaximum Ġmutual Ġinformation Ġ( L F - M MI ) Ġwith Ġcross - ent ropy Ġregular ization ĠB IB REF 14 . ĠRe - scoring Ġis Ġdone Ġusing Ġa Ġpr uned Ġtrig ram Ġlanguage Ġmodel Ġand Ġthe Ġsize Ġof Ġthe Ġdictionary Ġis Ġaround Ġ90 , 000 Ġwords . </s> Table ĠT AB REF 26 Ġshows Ġthe Ġresults Ġin Ġterms Ġof Ġword Ġerror Ġrate Ġ( WER ) Ġfor Ġdifferent Ġevaluated Ġmethods . ĠAs Ġcan Ġbe Ġseen , Ġthe Ġcreated Ġdatabase Ġcan Ġbe Ġused Ġto Ġtrain Ġwell Ġperforming Ġand Ġpractically Ġusable ĠPersian ĠAS R Ġmodels . </s> Con clusions </s> In Ġthis Ġpaper , Ġwe Ġhave Ġdescribed Ġthe Ġfinal Ġversion Ġof Ġa Ġlarge Ġspeech Ġcorpus , Ġthe ĠDeep Mine Ġdatabase . ĠIt Ġhas Ġbeen Ġcollected Ġusing Ġcrowds ourcing Ġand , Ġaccording Ġto Ġthe Ġbest Ġof Ġour Ġknowledge , Ġit Ġis Ġthe Ġlargest Ġpublic Ġtext - dependent Ġand Ġtext - prom pt ed Ġspeaker Ġverification Ġdatabase Ġin Ġtwo Ġlanguages : ĠPersian Ġand ĠEnglish . ĠIn Ġaddition , Ġit Ġis Ġthe Ġlargest Ġtext - independent Ġspeaker Ġverification Ġevaluation Ġdatabase , Ġmaking Ġit Ġsuitable Ġto Ġrobust ly Ġevaluate Ġstate - of - the - art Ġmethods Ġon Ġdifferent Ġconditions . ĠAlong side Ġthese Ġappealing Ġproperties , Ġit Ġcomes Ġwith Ġphone - level Ġtranscription , Ġmaking Ġit Ġsuitable Ġto Ġtrain Ġdeep Ġneural Ġnetwork Ġmodels Ġfor ĠPersian Ġspeech Ġrecognition . </s> We Ġprovided Ġseveral Ġevaluation Ġprotocols Ġfor Ġeach Ġpart Ġof Ġthe Ġdatabase . ĠThe Ġprotocols Ġallow Ġresearchers Ġto Ġinvestigate Ġthe Ġperformance Ġof Ġdifferent Ġmethods Ġin Ġvarious Ġscenarios Ġand Ġstudy Ġthe Ġeffects Ġof Ġchannels , Ġduration Ġand Ġphrase Ġtext Ġon Ġthe Ġperformance . ĠWe Ġalso Ġprovide Ġtwo Ġtest Ġsets Ġfor Ġspeech Ġrecognition : ĠOne Ġnormal Ġtest Ġset Ġwith Ġa Ġfew Ġminutes Ġof Ġspeech Ġfor Ġeach Ġspeaker Ġand Ġone Ġlarge Ġtest Ġset Ġwith Ġmore Ġ( 30 Ġminutes Ġon Ġaverage ) Ġspeech Ġthat Ġcan Ġbe Ġused Ġfor Ġany Ġspeaker Ġadaptation Ġmethod . </s> As Ġbaseline Ġresults , Ġwe Ġreported Ġthe Ġperformance Ġof Ġan Ġi - vector / H MM Ġbased Ġmethod Ġon ĠPersian Ġtext - dependent Ġpart . ĠMoreover , Ġwe Ġconducted Ġspeech Ġrecognition Ġexperiments Ġusing Ġconventional ĠH MM - based Ġmethods , Ġas Ġwell Ġas Ġstate - of - the - art Ġdeep Ġneural Ġnetwork Ġbased Ġmethod Ġusing ĠK aldi Ġtool kit Ġwith Ġpromising Ġperformance . ĠText - dependent Ġresults Ġhave Ġshown Ġthat Ġthe ĠDeep Mine Ġdatabase Ġis Ġmore Ġchallenging Ġthan ĠR SR 2015 Ġand ĠRed D ots Ġdatabases . </s> Acknowled gments </s> The Ġdata Ġcollection Ġproject Ġwas Ġmainly Ġsupported Ġby ĠSharif ĠDeep Mine Ġcompany . ĠThe Ġwork Ġon Ġthe Ġpaper Ġwas Ġsupported Ġby ĠCzech ĠNational ĠScience ĠFoundation Ġ( G AC R ) Ġproject Ġ" NE URE M 3 " ĠNo . Ġ19 - 269 34 X Ġand Ġthe ĠNational ĠProgramme Ġof ĠS ustain ability Ġ( N PU ĠII ) Ġproject Ġ" IT 4 In nov ations Ġexcellence Ġin Ġscience Ġ- ĠL Q 16 02 ".