多特征融合的英语口语考试自动评分系统的研究.docx

多特征融合的英语口语考试自动评分系统的研究
Introduction
Englishlanguageproficiencyhasbecomeasignificantdeterminantofacademic,professional,andpersonalsuccessintoday'sglobalizedworld.Therefore,languagetestingandassessmenthavereceivedincreasingattentionfromacademicinstitutions,languagetrainingcenters,andemployers.TraditionalEnglishlanguagetestsareadministeredmanuallybyqualifiedexaminers,whichcanbetime-consuming,expensive,andsubjective.Withtheadventoftechnology,automaticscoringsystemshavebeenproposedtoaddressthesedisadvantages.However,existingautomaticscoringsystemsoftenrelyonindividualfeaturesofspeech,suchaspronunciation,grammar,vocabulary,orfluency,whichcanbeinsufficientandinaccurate.Therefore,thispaperproposesamulti-featurefusion-basedEnglishoralexamautomaticscoringsystemtoimprovethereliabilityandvalidityoftheassessment.
Background
AutomaticscoringsystemsforEnglishoralexamshavebeendevelopedbasedonvariousapproaches.Forinstance,rule-basedsystemsutilizepre-definedscoringcriteriatoevaluatetheperformanceoftest-takersbasedoncertainfeatures,suchasintonation,stress,rhythm,orpauses.Statisticalmodels,suchassupportvectormachines(SVM),hiddenMarkovmodels(HMM),orneuralnetworks(NN),arealsocommonlyusedtoanalyzelargedatasetsofspeechfeaturesandpredictscores.Recently,deeplearningmodels,suchasconvolutionalneuralnetworks(CNN),recurrentneuralnetworks(RNN),ortransformernetworks,haveachievedstate-of-the-artperformanceinmanynaturallanguageprocessing(NLP)tasks,includingspeechrecognition,naturallanguageunderstanding,ormachinetranslation.Thesemodelscanextracthigh-levelabstractfeaturesfromspeechsignalsandcapturelong-termdependencies.However,mostoftheseapproachesfocusonindividualfeaturesanddonotconsidertheinterrelationshipsamongthem,whichcanlimittheirrobustnessandgeneralizability.
Methodology
Toovercomethelimitationsofpreviousapproaches,thispaperproposesamulti-featurefusion-basedEnglishoralexamautomaticscoringsystemthatintegratesdifferentfeaturesofspeech,suchasprosody,syntax,semantics,anddiscourse.Theproposedsystemco