电气工程及其自动化外文翻译文献

文献信息:文献标题：SpeechRecognitionUsingVectorQuantizationthroughModifiedK-meansLBGAlgorithm(基于改进矢量量化K-均值LBG算法的语音识别)国外作者：BalwantA.Sonkamble，DharmpalDoye文献出处：《ComputerEngineeringandIntelligentSystems》，2012,7(3)字数统计：英文2389单词，13087字符；中文3968汉字夕卜文文献：SpeechRecognitionUsingVectorQuantizationthrough

ModifiedK-meansLBGAlgorithmAbstractIntheVectorQuantization,themaintaskistogenerateagoodcodebook.Thedistortionmeasurebetweentheoriginalpatternandthereconstructedpatternshouldbeminimum.Inthispaper,aproposedalgorithmcalledModifiedK-meansLBGalgorithmusedtoobtainagoodcodebook.Thesystemhasshowngoodperformanceonlimitedvocabularytasks.Keywords:K-meansalgorithm,LBGalgorithm,VectorQuantization,SpeechRecognition1.IntroductionThenaturalwayofcommunicationamonghumanbeingsisthroughspeech.Manyhumanbeingsareexchangingtheinformationthroughmobilephonesaswellasothercommunicationtoolsinarealmanner[L.R.Rabineretal.,1993].TheVectorQuantization(VQ)isthefundamentalandmostsuccessfultechniqueusedinspeechcoding,imagecoding,speechrecognition,andspeechsynthesisandspeakerrecognition[S.Furui,1986].Thesetechniquesareappliedfirstlyintheanalysisofspeechwherethemappingoflargevectorspaceintoafinitenumberofregionsinthatspace.TheVQtechniquesarecommonlyappliedtodevelopdiscreteorsemi-continuousHMMbasedspeechrecognitionsystem.InVQ,anorderedsetofsignalsamplesorparameterscanbeefficientlycodedbymatchingtheinputvectortoasimilarpatternorcodevector(codeword)inapredefinedcodebook[[Tzu-ChuenLuetal.,2010].TheVQtechniquesarealsoknownasdataclusteringmethodsinvariousdisciplines.Itisanunsupervisedlearningprocedurewidelyusedinmanyapplications.Thedataclusteringmethodsareclassifiedashardandsoftclusteringmethods.Thesearecentroid-basedparametricclusteringtechniquesbasedonalargeclassofdistortionfunctionsknownasBregmandivergences[ArindamBanerjeeetal.,2005].Inthehardclustering,eachdatapointbelongstoexactlyoneofthepartitionsinobtainingthedisjointpartitioningofthedatawhereaseachdatapointhasacertainprobabilityofbelongingtoeachofthepartitionsinsoftclustering.Theparametricclusteringalgorithmsareverypopularduetoitssimplicityandscalability.Thehardclusteringalgorithmsarebasedontheiterativerelocationschemes.TheclassicalK-meansalgorithmisbasedonEuclideandistanceandtheLinde-Buzo-Gray(LBG)algorithmisbasedontheItakura-Saitodistance.Theperformanceofvectorquantizationtechniquesdependsontheexistenceofagoodcodebookofrepresentativevectors.Inthispaper,anefficientVQcodebookdesignalgorithmisproposedknownasModifiedK-meansLBGalgorithm.ThisalgorithmprovidessuperiorperformanceascomparedtoclassicalK-meansalgorithmandtheLBGalgorithm.Section-2describesthetheoreticaldetailsofVQ.Section-3elaboratesLBGalgorithm.Section-4explainsclassicalK-meansalgorithm.Section-5emphasizesproposedmodifiedK-meansLBGalgorithm.TheexperimentalworkandresultsarediscussedinSection-6andtheconcludingremarksmadeattheendofthepaper.VectorQuantizationThemainobjectiveofdatacompressionistoreducethebitratefortransmissionordatastoragewhilemaintainingthenecessaryfidelityofthedata.Thefeaturevectormayrepresentanumberofdifferentpossiblespeechcodingparametersincludinglinearpredictivecoding(LPC)coefficients,cepstrumcoefficients.TheVQcanbeconsideredasageneralizationofscalarquantizationtothequantizationofavector.TheVQencoderencodesagivensetofk-dimensionaldatavectorswithamuchsmallersubset.ThesubsetCiscalledacodebookanditselementsC.arecalledcodewords,codevectors,reproducingvectors,prototypesordesignsamples.Onlytheindexiistransmittedtothedecoder.Thedecoderhasthesamecodebookastheencoder,anddecodingisoperatedbytablelook-upprocedure.ThecommonlyusedvectorquantizersarebasedonnearestneighborcalledVoronoiornearestneighbourvectorquantizer.BoththeclassicalK-meansalgorithmandtheLBGalgorithmbelongtotheclassofnearestneighborquantizers.Akeycomponentofpatternmatchingisthemeasurementofdissimilaritybetweentwofeaturevectors.ThemeasurementofdissimilaritysatisfiesthreemetricpropertiessuchasPositivedefinitenessproperty,SymmetrypropertyandTriangularinequalityproperty.Eachmetrichasthreemaincharacteristicssuchascomputationalcomplexity,analyticaltractabilityandfeatureevaluationreliability.ThemetricsusedinspeechprocessingarederivedfromtheMinkowskimetric[J.S.Panetal.1996].TheMinkowskimetriccanbeexpressedasID(X,Y)=p宁g—y]p\i=1WhereX={x1,x2,...,xk}andY={y1,y2,...,yk}arevectorsandpistheorderofthemetric.TheCityblockmetric,EuclideanmetricandManhattanmetricarethespecialcasesofMinkowskimetric.Thesemetricsareveryessentialinthedistortionmeasurecomputationfunctions.Thedistortionmeasureisonewhichsatisfiesonlythepositivedefinitenesspropertyofthemeasurementofdissimilarity.ThereweremanykindsofdistortionmeasuresincludingEuclideandistance,theItakuradistortionmeasureandthelikelihooddistortionmeasure,andsoon.TheEuclideanmetric[Tzu-ChuenLuetal.,2010]iscommonlyusedbecauseitfitsthephysicalmeaningofdistanceordistortion.Insomeapplicationsdivisioncalculationsarenotrequired.Toavoidcalculatingthedivisions,thesquaredEuclideanmetricisemployedinsteadoftheEuclideanmetricinpatternmatching.Thequadraticmetric[MarcelR.Ackermannetal.,2010]isanimportantgeneralizationoftheEuclideanmetric.Theweightedcepstraldistortionmeasureisakindofquadratecmetric.Theweightedcepstraldistortionkeyfeatureisthatitequalizestheimportanceineachdimensionofcepstrumcoefficients.Inthespeechrecognition,theweightedcepstraldistortioncanbeusedtoequalizetheperformanceoftherecognizeracrossdifferenttalkers.TheItakura-Saitodistortion[ArindamBanerjeeetal.,2005]measurecomputesadistortionbetweentwoinputvectorsbyusingtheirspectraldensities.TheperformanceofthevectorquantizercanbeevaluatedbyadistortionmeasureDwhichisanon-negativecostD(X,X.)associatedwithquantizinganyinputvectorX.withareproductionvectoX..Usually,theEuclideandistortionmeasureisused.TheperformanceofaquantizerisalwaysqualifiedbyanaveragedistortionD E[D(X.,X.)]etweentheinputvectorsandthefinalreproductionvectors,whereErepresentstheexpectationoperator.Normally,theperformanceofthequantizerwillbegoodiftheaveragedistortionissmall.AnotherimportantfactorinVQisthecodewordsearchproblem.Asthevectordimensionincreasesaccordinglythesearchcomplexityincreasesexponentially,thisisamajorlimitationofVQcodewordsearch.Itlimitsthefidelityofcodingforrealtimetransmission.AfullsearchalgorithmisappliedinVQencodingandrecognition.Itisatimeconsumingprocesswhenthecodebooksizeislarge.Inthecodewordsearchproblem,assigningonecodewordtothetestvectormeansthesmallestdistortionbetweenthecodewordandthetestvectoramongallcodewords.GivenonecodewordCandthetestvectorXinthek-dimensionalspace,thedistortionofthesquaredEuclideanmetriccanbeexpressedasfollows:D(X,C)MO•-c)2i=1WhereC={ci,c2, ,ck}andX={x1,,x2, ,xk}Therearethreewaysofgeneratinganddesigningagoodcodebooknamelytherandommethod,thepair-wisenearestneighborclusteringandthesplittingmethod.Awidevarietyofdistortionfunctions,suchassquaredEuclideandistance,Mahalanobisdistance,Itakura-Saitodistanceandrelativeentropyhavebeenusedforclustering.TherearethreemajorproceduresinVQ,namelycodebookgeneration,encodingprocedureanddecodingprocedure.TheLBGalgorithmisanefficientVQclusteringalgorithm.Thisalgorithmisbasedeitheronaknownprobabilisticmodeloronalongtrainingsequenceofdata.Linde-Buzo-Gray(LBG)algorithmTheLBGalgorithmisalsoknownastheGeneralisedLloydalgorithm(GLA).Itisaneasyandrapidalgorithmusedasaniterativenonvariationaltechniquefordesigningthescalarquantizer.Itisavectorquantizationalgorithmtoderiveagoodcodebookbyfindingthecentroidsofpartitionedsetsandtheminimumdistortionpartitions.InLBG,theinitialcentroidsaregeneratedfromallofthetrainingdatabyapplyingthesplittingprocedure.Allthetrainingvectorsareincorporatedtothetrainingprocedureateachiteration.TheGLAalgorithmisappliedtogeneratethecentroidsandthecentroidscannotchangewithtime.TheGLAalgorithmstartsfromoneclusterandthenseparatesthisclustertotwoclusters,fourclusters,andsoonuntilNclustersaregenerated,whereNisthedesirednumberofclustersorcodebooksize.Therefore,theGLAalgorithmisadivisiveclusteringapproach.Theclassificationateachstageusesthefull-searchalgorithmtofindthenearestcentroidtoeachvector.TheLBGisalocaloptimizationprocedureandsolvedthroughvariousapproachessuchasdirectedsearchbinary-splitting,mean-distance-orderedpartialcodebooksearch[Lindeetal.,1980,Modhaetal.,2003],enhanceLBG,GA-basedalgorithm[Tzu-ChuenLuetal.,2010,Chin-ChenChangetal.2006],evolution-basedtabusearchapproach[Shih-MingPanetal.,2007],andcodebookgenerationalgorithm[Buzoetal.,1980].Inspeechprocessing,vectorquantizationisusedforinstanceofbitstreamreductionincodingorinthetasksbasedonHMM.Initializationisanimportantstepinthecodebookestimation.TwoapproachesusedforinitializationareRandominitialization,whereLvectorsarerandomlychosenfromthetrainingvectorsetandInitializationfromasmallercodingbookbysplittingthechosenvectors.ThedetailedLBGalgorithmusingunknowndistributionisdescribedasgivenbelow:Step1:Designa1-vectorcodebook.Setm=1.CalculatecentroidC=-zTXWhereTisthetotalnumberofdatavectors.Step2:Doublethesizeofthecodebookbysplitting.DivideeachcentroidCintotwoclosevectorsC=Cx(1+8)andC.=Cx(1-5),1<i<m.Here8isasmallfixedperturbationscalar.Letm=2m.Setn=0,herenistheiterativetime.Step3:Nearest-NeighborSearch.Findthenearestneighbortoeachdatavector.PutX.inthepartitionedsetPifCisthenearestneighbortoX.Step4:FindAverageDistortion.AfterobtainingthepartitionedsetsP=(P,1<i<m),Setn=n+1iCalculatetheoverallaveragedistortionD广T以呵(D：i),C)WhereP={X(),X(),……,X()}i1 2 TStep5:CentroidUpdate.FindcentroidsofalldisjointpartitionedsetsPbyiC=—zTiX(i)1T j-ijiStep6:Iteration1.If(D-D)/D>s,gotostep3;otherwisegotostep7and£isathreshold.Step7:Iteration2.Ifm=N,thentakethecodebookCasthefinalcodebook;otherwise,gotostep2.HereNisthecodebooksize.TheLBGalgorithmhaslimitationslikethequantizedspaceisnotoptimizedateachiterationandthealgorithmisverysensitivetoinitialconditions.ClassicalK-meansAlgorithmTheK-meansalgorithmisproposedbyMacQueenin1967.Itisawellknowniterativeprocedureforsolvingtheclusteringproblems.ItisalsoknownastheC-meansalgorithmorbasicISODATAclusteringalgorithm.Itisanunsupervisedlearningprocedurewhichclassifiestheobjectsautomaticallybasedonthecriteriathatminimumdistancetothecentroid.IntheK-meansalgorithm,theinitialcentroidsareselectedrandomlyfromthetrainingvectorsandthetrainingvectorsareaddedtothetrainingprocedureoneatatime.Thetrainingprocedureterminateswhenthelastvectorisincorporated.TheK-meansalgorithmisusedtogroupdataandthegroupscanchangewithtime.ThealgorithmcanbeappliedtoVQcodebookdesign.TheK-meansalgorithmcanbedescribedasfollows:Step1:RandomlyselectNtrainingdatavectorsastheinitialcodevectorsC,i=1,2, ,NfromTtrainingdatavectors.Step2:ForeachtrainingdatavectorX.,j=1,2, ,TassignX.tothepartitionedsetSifi=argminD(X,C)Step3:ComputethecentroidofthepartitionedsetthatiscodevectorusingWhere\s\denotesthenumberoftrainingdatavectorsinthepartitionedsetS.Ifthereisnochangeintheclusteringcentroids,thenterminatetheprogram;otherwise,gotostep2.TherearevariouslimitationsofK-meansalgorithm.Firstly,itrequireslargedatatodeterminethecluster.Secondly,thenumberofcluster,K,mustbedeterminedbeforehand.Thirdly,ifthenumberofdataisasmallitdifficulttofindrealclusterandlastly,asperassumptioneachattributehasthesameweightanditquitedifficulttoknowswhichattributecontributesmoretothegroupingprocess.Itisanalgorithmtoclassifyortogroupobjectsbasedonattributes/featuresintoKnumberofgroup.Kispositiveintegernumber.Thegroupingisdonebyminimizingthesumofsquaresofdistancesbetweendataandthecorrespondingclustercentroid.ThemainaimofK-meanclusteringistoclassifythedata.Inpractice,thenumberofiterationsisgenerallymuchlessthanthenumberofpoints.ProposedModifiedK-meansLBGAlgorithmTheproposedalgorithmsobjectiveistoovercomethelimitationsofLBGalgorithmandK-meansalgorithm.TheproposedmodifiedKmeansLBGalgorithmisthecombinationofadvantagesofLBGalgorithmandK-meansalgorithms.TheKmeansLBGalgorithmisdescribedasgivenbelow:Step1:RandomlyselectNtrainingdatavectorsastheinitialcodevectors.Step2:Calculatetheno.ofcentroids.Step3:Doublethesizeofthecodebookbysplitting.Step4:Nearest-NeighborSearch.Step5:FindAverageDistortion.Step6:Updatethecentroidtillthereisnochangeintheclusteringcentroids,terminatetheprogramotherwisegotostep1.^.ExperimentationandResultsTheTI46database[NIST,1991]isusedforexperimentation.Thereare16speakersfromthem8malespeakersand8femalespeakers.Thenumbersofreplicationsare26forutterancebyeachperson.Thetotaldatabasesizeis4160utterancesofwhich1600sampleswereusedfortrainingandremainingsamplesareusedfortestingof10wordsthatarenumbersinEnglish1to9and0aresampledatarateof8000Hz.Afeaturevectorof12-dimensionalLinearPredictingCodingCepstrumcoefficientswasobtainedandprovidedasaninputtovectorquantizationtofindcodewordsforeachclass.TherearefivefiguresshowscomparativegraphsofthedistortionmeasureobtainedusingLBGalgorithmandK-meansalgorithmandproposedK-meansLBGalgorithm.ThedistortionmeasureobtainedbytheproposedalgorithmissmallestascomparedtotheK-meansalgorithmandtheLBGalgorithm.TheproposedmodifiedKmeanLBGalgorithmgivesminimumdistortionmeasureascomparedtoK-meansalgorithmandLBGalgorithmtoincreasetheperformanceofthesystem.Thesmallestmeasuregivessuperiorperformanceascomparedtoboththealgorithmsasisincreasedbyabout1%to4%foreverydigit.7.ConclusionTheVectorQuantizationtechniquesareefficientlyappliedinthedevelopmentofspeechrecognitionsystems.Inthispaper,theproposedanovelvectorquantizationalgorithmcalledK-meansLBGalgorithm.Itisusedefficientlytoincreasetheperformanceofthespeechrecognitionsystem.TherecognitionaccuracyobtainedusingK-meansLBGalgorithmisbetterascomparedtoK-meansandLBGalgorithm.TheaveragerecognitionaccuracyofK-meansLBGalgorithmismorethan2.55%usingK-meansalgorithmwhiletheaveragerecognitionaccuracyofK-meansLBGalgorithmismorethan1.41%usingLBGalgorithm.

DistortionmesaLirctorcontroidK=4usingK-means,LP,GandZEC-mcQnsLP.G-ctlgonthm肖SEWa肖SEWau-sssMU.3bU.3O.?0.052 34567S9OALL—A—K-meanEiLBG2 34567S9OALL—A—K-meanEiLBG■LBG.K-meansjdiisrtci-'ti■□u.imeaspireFi：n~ id3Cjdiisrtci-'ti■□u.imeaspireFi：n~ id3C—MuMirigEC-mmm二与〉sai■:' an»UB-(3-al^oritlT.ii-Figure2.ComparativegraphforcentroidK=8Figure4.ComparativegraphforcentroidK=32T?ji^Li_oLicwiiiiclil3_ii-cforccTiLivjidTC=3Zu^>izi^IC-ixicliiit;,undIC-uFigure4.ComparativegraphforcentroidK=32T?ji^Li_oLicwiiiiclil3_ii-cforccTiLivjidTC=3Zu^>izi^IC-ixicliiit;,undIC-uiuliiiuLjB-Oal^oi'iLlnnI2>i^LviLiun Lvicd<i】Liurd上二ubaixE2iin&9<3*sxnd-TLin^uiiAbL^O'ixl)=；k/i.xUmiiDigitsFigure5.ComparativegraphforcentroidK=64MS图gwsM中文译文：基于改进矢量量化K-均值LBG算法的语音识别摘要矢量量化的主要任务是产生良好的码本。原始图案和重建模式之间的失真度量应该是最小的。在本文中，提出使用的算法称为改进的K-均值LBG算法，取得了良好的码本。该方法在小词汇量任务中有很好的表现。关键词：K-均值算法；LBG算法；矢量量化；语音识别引言讲话是沟通人与人之间最自然的方式。很多人都是通过手机以及其他的通讯工具用一个真实的方式进行交换信息［L.R.Rabineretal.,1993］。矢量量化（VQ）是语音编码，图像编码，语音识别，语音合成和说话人识别中使用的最基本的和最成功的技术［S.Furui,1986］。这些技术在讲话中首先分析应用，其中大型向量空间映射到有限数量的区域空间。VQ技术通常应用于开发离散或半连续HMM的语音识别系统。在VQ中，信号样本或参数的有序集合可以有效地匹配输入矢量在预定的类似码本的模式或码矢（码字）的编码［Tzu-ChuenLuetal.,2010］。在各种学科中VQ技术也被称为数据聚类方法。它是一个无监督的学习过程，被广泛用于许多应用中。数据聚类方法可归类为硬质和软聚类方法。这些都是基于一个称为布雷格曼分歧［ArindamBanerjeeetal.,2005］失真函数质心为基础的参数化聚类技术的大类。在硬聚类中每一个数据点获得的数据属于不相交的分割，而每一个数据点具有一定的概率是属于具有各分区的软聚类的分区中的一个。该参数的聚类算法由于它的简单性和可扩展性非常受欢迎。硬聚类算法是基于迭代搬迁方案。经典的K-均值算法是基于欧氏距离和基于板仓斋藤距离的Linde-Buzo-Gray（LBG）算法。矢量量化技术的性能取决于代表矢量的码本的良好的存在。在本文中，一个高效的VQ码本设计算法称为改进K-均值LBG算法。该算法相比传统的K-均值算法和LBG算法具有优越的性能。第2节描述了矢量量化的理论细节。第3节阐述LBG算法。第4节解释经典的K-均值算法。第5节强调提出改进的K-均值LBG算法。实验工作和结果将在第6节和文末提出的结论性意见进行讨论。矢量量化数据压缩的主要目标是减少比特率的传输或存储数据，同时保持必要的数据的保真度。特征向量可以表示包括线性预测编码（LPC）系数在内的多个不同的可能的语音编码参数，以便对倒谱系数的确定。VQ可以看作标量量化的一种矢量量化的概括。VQ编码器编码一个给定的k维数据向量与一个小得多的子集。该子集C被称为码本，它的元素匕被称为码字、码向量、向量复制、原型或设计样品。唯一的索引i被发送到解码器。该解码器具有相同的码本作为编码器和解码是由查表程序操作。常用的矢量量化是基于称为最近邻Voronoi图或最近邻矢量量化。无论是经典的K-均值算法还是LBG算法都属于类近邻量化。模式匹配的一个关键组成部分是两个特征向量之间相异的测量。相异的测量满足几个性能指标，如对称性和三角不等式性质。每个指标有三个主要特点，如计算复杂性，分析性和可追踪性功能评估的可靠性。在语音处理中使用的指标是闵可夫斯基度量［J.S.Panetal.1996］。闵可夫斯基度量可以表示为Dp(X,Y)=pfg-叶i=1其中X={x1,X2,...,xk}和Y={y1,y2,...,yk}是矢量，p为度量的顺序。欧氏度量和曼哈顿度量是特殊情况的闵可夫斯基指标。这些指标在失真测量功能计算中都是十分必要的。失真度量只是一种满足相异测量的正定属性。有许多种变形的措施，包括欧氏距离，板仓失真测度和似然失真测量等等。欧氏度量［Tzu-ChuenLuetal.,2010］是最常用的，因为它适合距离或变形的物理意义。在某些应用中除法的运算不是必需的。为了避免计算分类，平方欧氏度量采用欧氏度量的模式匹配来代替。二次度量［MarcelR.Ackermannetal.,2010是欧几里德度量的一个重要的概括。加权倒谱失真测度是一种quadratec度量。加权倒谱失真的关键特征是，它均衡的对倒谱系数的每个维度的重要性。在语音识别中，加权倒谱失真，可用于平衡不同的说话者识别器的性能。板仓齐藤失真［ArindamBanerjeeetal.,2005］通过使用他们的频谱密度测量计算两个输入向量之间的失真。矢量量化器的性能可以通过一个失真度量D，它是一个非负的成本D(X,X)，与量化任何输入矢量X.中的再现矢量X.关联进行评估。通常情况jj j j下，欧几里得失真度量被使用。一个量化器的性能总是限定输入矢量和最后再现的载体，其中E表示期望算子之间的平均失真D=E［D(Xj,X顶)］。通常情况下，如果平均失真小，则对量化器的性能将是一件好事。矢量量化的另一重要因素是该码字的搜索问题。矢量维数成倍增加，相应的搜索复杂性随之增加，这是VQ码字搜索的一个主要限制。它限制了编码实时传输的保真度。一个完整的搜索算法是应用在VQ编码和识别中。当码本大小是大的时候，这是一个耗时的过程。关于码字搜索问题，分配一个码字的测试向量表示所有码字之间的码字和所述测试向量之间的最小失真。给定一个代码字匕和测试向量X的k维空间，平方欧几里德度量的畸变可以表示为如下：D(X,C)=寸(乃-c)2i=1其中C={cl,C2, ,ck}和X={x1,,X2,,xk}有三种方式产生并设计一个好的码本，即随机法，成对最近邻聚类和分割方法。各种各样的变形的功能，如平方欧氏距离，马氏距离，板仓齐藤距离和相对熵已被用于聚类。在VQ中有三个主要环节，即码本的生成，编码程序和解码程序。LBG算法是一种有效的量化聚类算法。这种算法要么是基于一个已知的概率模型或是一个长训练序列的数据。Linde-Buzo-Gray(LBG)算法LBG算法也被称为广义的劳埃德算法(GLA)。它是用来作为设计标量量化器的迭代非变分技术的简易且快速的运算法则。这种矢量量化算法通过寻找分配集和最小失真分割的质心而得出一个好的码本。在LBG算法中，从所有训练数据的应用分裂过程中产生初始质心。在每次迭代过程中所有训练矢量被纳入到判别过程中。GLA算法应用于生成的质心和质心能不随时间变化。GLA算法开始于一个集群，然后分离该集群到两个、四个集群，依此类推，直至所生成的N个集群，其中N是群或码本大小的所需数量。因此，GLA算法是一个分裂的聚类方法。每个阶段的分类使用全搜索算法来找到每一个向量最近的质心。LBG是一个局部优化过程，通过各种方法，如定向搜索二进制分解解决，均值距离排序的局部码本