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1、RecentAdvancesintheAutomaticRecognitionofAudiovisualSpeechGERASIMOSPOTAMIANOS,MEMBER,IEEE,CHALAPATHYNETI,MEMBER,IEEE,GUILLAUMEGRAVIER,ASHUTOSHGARG,MEMBER,IEEE,ANDANDREWW.SENIOR,SENIORMEMBER,IEEEInvitedPaperVisualspeechinformationfromthespeaker’smouthregionhasareenvisionedtousespeech,among
2、othermeans,toachievebeensuccessfullyshowntoimprovenoiserobustnessofautomaticnatural,pervasive,andubiquitouscomputing.However,speechrecognizers,thuspromisingtoextendtheirusabilityinalthoughASRhaswitnessedsignificantprogressinwell-de-thehumancomputerinterface.Inthispaper,wereviewthemainfine
3、dapplicationslikedictationandmediumvocabularycomponentsofaudiovisualautomaticspeechrecognition(ASR)andpresentnovelcontributionsintwomainareas:first,thevisualtransactionprocessingtasksinrelativelycontrolledenviron-front-enddesign,basedonacascadeoflinearimagetransformsments,itsperformanceha
4、syettoreachthelevelrequiredforofanappropriatevideoregionofinterest,andsubsequently,speechtobecomeatrulypervasiveuserinterface.Indeed,audiovisualspeechintegration.Onthelattertopic,wediscussnewevenin“clean”acousticenvironments,state-of-the-artASRworkonfeatureanddecisionfusioncombination,the
5、modelingsystemperformancelagshumanspeechperceptionbyuptoofaudiovisualspeechasynchrony,andincorporatingmodalityreliabilityestimatestothebimodalrecognitionprocess.Wealsoanorderofmagnitude[1].Moreover,itslackofrobustnessbrieflytouchupontheissueofaudiovisualadaptation.Weapplytochannelandenvir
6、onmentnoisecontinuestobeamajorouralgorithmstothreemultisubjectbimodaldatabases,ranginghindrance[2],[3].Clearly,nontraditionalapproachesthatfromsmall-tolarge-vocabularyrecognitiontasks,recordedusesourcesofinformationorthogonaltotheaudioinputareinbothvisuallycontrolledandchallengingenvironm
7、ents.OurneededtoachieveASRperformanceclosertothehumanexperimentsdemonstratethatthevisualmodalityimprovesASRoverallconditionsanddataconsidered,thoughlesssoforvisuallyspeechperceptionlevel,androbustenoughtobedeployablechallengingenvironmentsandlargevocabularytasks.inf
