简介：说出的比赛是为一种语言或一个通讯系统的自我组织的出现的nonequilibrium动力学的一个模型。我们学习扬声器与与exp成正比的可能性从它的库存在选择一个词的最小的说出游戏的一个修改版本(R*一)，在R是名字的成功比率的地方并且一是一个悦耳的参数。由调查效果一上为方形的格子和没有规模的网络的进化过程，我们发现集中时间与增加减少一上二联网，它显示成功的词的优先的选择能加速到达一致。更有趣地，为一>0，我们发现在集中之间的关系预定并且一展览一种幂定律形式。[从作者抽象]

简介：Speakervariabilityisanimportantsourceofspeechvariationswhichmakescontinuousspeechrecognitionadifficulttask.Adaptingautomaticspeechrecognition(ASR)modelstothespeakervariationsisawell-knownstrategytocopewiththechallenge.AlmostallsuchtechniquesfocusondevelopingadaptationsolutionswithintheacousticmodelsoftheASRsystems.Althoughvariationsoftheacousticfeaturesconstituteanimportantportionoftheinter-speakervariations,theydonotcovervariationsatthephoneticlevel.Phoneticvariationsareknowntoformanimportantpartofvariationswhichareinfluencedbybothmicro-segmentalandsuprasegmentalfactors.Inter-speakerphoneticvariationsareinfluencedbythestructureandanatomyofaspeaker'sarticulatorysystemandalsohis/herspeakingstylewhichisdrivenbymanyspeakerbackgroundcharacteristicssuchasaccent,gender,age,socioeconomicandeducationalclass.Theeffectofinter-speakervariationsinthefeaturespacemaycauseexplicitphonerecognitionerrors.Theseerrorscanbecompensatedlaterbyhavingappropriatepronunciationvariantsforthelexiconentrieswhichconsiderlikelyphonemisclassificationsbesidespronunciation.Inthispaper,weintroducespeakeradaptivedynamicpronunciationmodels,whichgeneratedifferentlexiconsforvariousspeakerclustersanddifferentrangesofspeechrate.Themodelsarehybridsofspeakeradaptedcontextualrulesanddynamicgeneralizeddecisiontrees,whichtakeintoaccountwordphonologicalstructures,rateofspeech,unigramprobabilitiesandstresstogeneratepronunciationvariantsofwords.EmployingthesetofspeakeradapteddynamiclexiconsinaFarsi(Persian)continuousspeechrecognitiontaskresultsinworderrorratereductionsofasmuchas10.1%inaspeaker-dependentscenarioand7.4%inaspeaker-independentscenario.