Abstract:Inordertoextractthestablelong-termfeatureswhenthedataisinsufficient,agroup-Lassobasedmixturediscriminantanalysismethodwas proposed.Firstly,theGaussianmixturemodelwas used todescribethedistributionofdata,andtheobjectivefunctionofgroup-Lassobasedmixturediscriminantanalysiswasgotbasedonthequadraticvariationalformofthegroup-Lasso.Subsequently,throughdefiningtheblurredresponsematrix,theproblemofsolvingthediscriminantanalysistransformmatrixwasfiguredoutbyeffectivelycombinedwiththeoptimalscoringmethod.Finally,thesuper-vectorwasobtainedbyconjoinedtheadjacentframesMelfilterbankoutput,andthetime-frequencyfeaturewasextractedafterthedimensionalityofsuper-vectorreducedusingthetransformmatrix.Theexperimentalresultsshowedthat thismethodcanobtainsparsetransformationmatrix.ComparedtothePLDAandSLDAdiscriminantanalysismethod,therecognitionaccuracyrateisincreasedby0.71%and1.53%respectively,andwhenlackofdata,thismethodcanachievehigherrecognitionperformance.