改进的智能机器人语音识别方法_英文_

第21卷第6期2009年12月重庆邮电大学学报自然科学版JOURNALOFCHONGQINGUNIVERSITYOFPOSTSANDTELECOMMUNICATIONSNATURALSCIENCEEDITIONVOL21NO6DEC2009IMPROVEDSPEECHRECOGNITIONMETHODFORINTELLIGENTROBOTZHANGYI,LIYAN2HUA,LIUQUAN2JIE,YANGHONG2MEI,ZENGLIRESEARCHCENTEROFINTELLIGENTSYSTEMANDROBOTICSCHONGQINGUNIVERSITYOFPOSTSANDTELECOMMUNICATIONS,CHONGQING400065,PRCHINAABSTRACTASACOMMUNICATIONTECHNOLOGYBETWEENMANMACHINEINTERACTIVETECHNOLOGY,SPEECHRECOGNITIONISWIDELYUSEDINTHISPAPER,WEINTRODUCESTHESPEECHRECOGNITIONSYSTEMBASEDONTHE16BITSPCE061ASERIALSINGLECHIPANDPROPOSEASPEECHRECOGNITIONAPPROACHWITHMIXEDPARAMETER,WHICHCOMBINESTHETRADITIONALLINEARPREDICTIVECEPSTRALCOEFFICIENTSLPCCANDFRACTALFEATUREASTHEFEATUREPARAMETERTHELPCCMETHODISLINEARPROCEDUREBASEDONTHEASSUMPTIONTHATSPEAKERFEATURESHAVEPROPERTIESCAUSEDBYTHEVOCALTRACTRESONANCESFRACTALDIMENSIONISUSEDTOQUANTITATIVELYDESCRIBETHECHAOSNONLINEARITYINSPEECHAIRFLOWTHEEXPERIMENTALRESULTSSHOWTHATMIXEDFEATUREPARAMETEROFLPCCANDFRACTALDIMENSIONISBETTERTHANSINGLELPCCFEATUREPARAMETERINRECOGNITIONRATEKEYWORDSSPEECHRECOGNITIONLPCCFRACTALDIMENSIONINTELLIGENTROBOTARTICALID16732825X20090620799207CLCNUMBERTP242DOCUMENTCODEA改进的智能机器人语音识别方法张毅,李艳花,刘全杰,杨红梅,曾莉重庆邮电大学智能系统及机器人研究所,重庆400065摘要作为一种人机信息交互技术,语音识别技术得到了广泛的应用。介绍了基于凌阳十六位单片机SPCE061A的语音识别系统,并且采用了以传统的线性预测倒谱系数LPCC与分形维数相结合的混合参数作为特征参数的语音识别方法。LPCC方法是体现说话人特定的声道共振特性的线性预测方法,而分形维数则可以定量的描述语音气流中的非线性混沌特征。实验结果表明,基于LPCC与分形维数混合参数的语音识别方法要比单一的LPCC参数语音识别方法识别效果好。关键词语音识别LPCC分形维数智能机器人IBLEINTERACTIONFORTHEEMBEDDEDSYSTEMSSPEECHCONTROLISDEPENDENTONTHETECHNOLOGYOFAUTOMATICSPEECHRECOGNITION,THETASKOFWHICHCANBESPEAKERDEPENDENTORSPEAKERINDEPENDENT1GENER2ALLY,SPEAKER2INDEPENDENTSYSTEMISMOREWIDELYUSED,SINCETHEUSERISNOTREQUIREDTOCONDUCTTHETRAININGSPEECHRECOGNITIONCANBEALSODIVIDEDINTOISOLATEDWORDRECOGNITION,CONNECTEDWORDRECOGNITIONORLARGEVOCABULARYCONTINUOUSRECOGNITIONINTHISPAPER,WEPRESENTOURWORKOFDEPLOYINGASPEAKERINDEPENDENTSPEECHRECOGNITIONSYSTEMFORAINTELLIGENTROBOTSPEECHHASNON2LINEARANDNON2STATIONARYCHARAC2TERISTICS2EXPERIMENTALEVIDENCEEXISTSTHATPROVES1INTRODUCTIONWITHTHEDEVELOPMENTOFMODERNSCIENCEANDCOM2PUTERTECHNOLOGY,PEOPLENEEDAMOREADVANTAGEOUSANDNATURALMETHODTOCOMMUNICATEWITHTHEMACHINER2YINTERMSOFTHIS,LANGUAGEISTHEMOSTIMPORTANTANDEFFECTIVEINFORMATIONSOURCEESPECIALLYINTHEINTELLI2GENTROBOTSYSTEM,SPEECHRECOGNITIONATTRACTSMUCHAT2TENTIONDUETOITSBROADAPPLICATIONASTHEMOSTNATU2RALANDEXPRESSIVEMEANSOFCOMMUNICATION,SPEECHISASUITABLECHOICEFORTHEHUMAN2COMPUTERINTERACTIONSOSPEECHHASTHEPOTENTIALTOPROVIDEADIRECTANDFLEX2RECEIVEDDATE2009204217MODIFICATIONDATE2009209212THATSPEECHSIGNALISPRODUCEDBYANON2LINEARDYNAMI2CALSYSTEMTHATOFTENGENERATESSMALLORLARGEDEGREEOFTURBULENCEINTHISPAPERASPEAKERIDENTIFICATIONISPERFORMEDUSINGACOMBINATIONOFLINEARPREDICTIVECEPSTRALCOEFFICIENTSLPCCWITHANONLINEARDYNAM2ICINVARIANTTHEFRACTALDIMENSIONWHILETHEUSEOFLINEARPREDICTIVECEPSTRALCOEFFICIENTSLPCCCANPERFORMASPEAKERRECOGNITIONQUITESUCCESSFULLY,ITMAYNOTBEACCURATEENOUGHFORSOMEAPPLICATIONSTHECHARACTERIZATIONOFASPEAKERUSINGANONLINEARDYNAMICDESCRIPTIONCANHELPONIDENTIFYINGPEOPLEFROMTHEIRVOICESTHEASSUMPTIONSUSEDTOEXTRACTTHESTANDARDFEATUREPARAMETERSDONOTDESCRIBETHENONLINEARDY2NAMICEVOLUTIONOFTHESYSTEMITWILLBESHOWNTHATADDNONLINEARDYNAMICQUALITATIVEINFORMATIONTOTHESTANDARDFEATUREPARAMETERS,SUCHASFRACTALDIMEN2SION,ISEQUIVALENTTOADDSPEAKER2DEPENDENTFEATURES,NOTPRESENTINTHESTANDARDFEATUREPARAMETERSSOFARTHISCOMBINATIONWILLLEADASPEECHRECOGNITIONSYSTEMTOMOREACCURATERESULTSBECAUSEOFCHAOSCHARACTERSINSPEECHAIRFLOW,FRACTALCANBEUSEDTOQUANTIFYTHECHAOTICPHENOMENONINSPEECHSIGNALS324SPEECHWAVEAPPEARSINFRAC2TALFEATURE,FRACTALUSEDTOIMPROVETHETECHNIQUEOFSPEECHRECOGNITIONWILLBEMOREIMPORTANTTHETRADI2TIONALLINEARFEATUREPARAMETER,SUCHASLPCC,CANNOTREPRESENTNONLINEARFEATUREOFSPEECHSOINORDERTOREPRESENTTHEFEATUREOFSPEECHBETTERANDAVOIDTHELO2NALISACOMPLEXNONLINEARPROCESSIFTHESTUDYOFSPEECHRECOGNITIONWANTSTOBREAKTHROUGH,NONLINEARSYSTEMTHEORYMETHODMUSTBEINTRODUCEDTOITRE2CENTLY,WITHTHEDEVELOPMENTOFNONLINEAR2SYSTEMTHEO2RIESSUCHASARTIFICIALNEURALNETWORKSANN,CHAOSANDFRACTAL,ITISPOSSIBLETOAPPLYTHESETHEORIESTOSPEECHRECOGNITIONTHEREFORE,THESTUDYOFTHISPAPERISBASEDONANNANDCHAOSANDFRACTALTHEORIESAREIN2TRODUCEDTOPROCESSSPEECHRECOGNITIONSPEECHRECOGNITIONISDIVIDEDINTOTWOWAYSTHATARESPEAKERDEPENDENTANDSPEAKERINDEPENDENTSPEAKERDEPENDENTREFERSTOTHEPRONUNCIATIONMODELTRAINEDBYASINGLEPERSON,THEIDENTIFICATIONRATEOFTHETRAININGPERSONSORDERSISHIGH,WHILEOTHERSORDERSISINLOWIDENTIFICATIONRATEORCANTBERECOGNIZEDSPEAKERINDEPENDENTREFERSTOTHEPRONUNCIATIONMODELTRAINEDBYPERSONSOFDIFFERENTAGE,SEXANDREGION,ITCANIDENTIFYAGROUPOFPERSONSORDERSGENERALLY,SPEAKERINDEPENDENTSYSTEMISMOREWIDELYUSED,SINCETHEUSERISNOTREQUIREDTOCONDUCTTHETRAININGSOEX2TRACTIONOFSPEAKERINDEPENDENTFEATURESFROMTHESPEECHSIGNALISTHEFUNDAMENTALPROBLEMOFSPEAKERRECOGNITIONSYSTEMSPEECHRECOGNITIONCANBEVIEWEDASAPATTERNREC2OGNITIONTASK,WHICHINCLUDESTRAININGANDRECOGNITIONGENERALLY,SPEECHSIGNALCANBEVIEWEDASATIMESE2QUENCEANDCHARACTERIZEDBYTHEPOWERFULHIDDENMARK2OVMODELHMMTHROUGHTHEFEATUREEXTRACTION,THESPEECHSIGNALISTRANSFERREDINTOFEATUREVECTORSANDACTASOBSERVATIONSINTHETRAININGPROCEDURE,THESEOBSER2VATIONSWILLFEEDTOESTIMATETHEMODELPARAMETERSOFHMMTHESEPARAMETERSINCLUDEPROBABILITYDENSITYFUNCTIONFORTHEOBSERVATIONSANDTHEIRCORRESPONDINGSTATES,TRANSITIONPROBABILITYBETWEENTHESTATES,ETCAFTERTHEPARAMETERESTIMATION,THETRAINEDMODELSCANBEUSEDFORRECOGNITIONTASKTHEINPUTOBSERVATIONSWILLBERECOGNIZEDASTHERESULTEDWORDSANDTHEACCU2RACYCANBEEVALUATEDTHEWHOLEPROCESSISILLUSTRATEDINFIG1CALIZATIONOFUSINGSUBSECTIONLINEARMETHOD,LPCCCOMBINEDWITHFRACTALFEATUREFORSPEECHRECOGNITIONISHERE,BYCHAOSCHARACTERISTICSOFSPEECHSIGNAL,SPEECHFRACTALDIMENSIONASONEFEATUREPARAMETERINRECOGNITIONTOCOMBINEWITHTHETRADITIONALLINEARFEA2TUREPARAMETERLPCC,WEPRESENTASPEECHRECOGNITIONMETHODWITHMIXEDFEATUREPARAMETERTOIMPROVERECOGNITIONPERFORMANCETHE2OVERVIEWOFSPEECHRECOGNITIONSPEECHRECOGNITIONHASRECEIVEDMOREANDMOREATTENTIONRECENTLYDUETOTHEIMPORTANTTHEORETICALMEANINGANDPRACTICALVALUE5UPTONOW,MOSTSPEECHRECOGNITIONISBASEDONCONVENTIONALLINEARSYS2TEMTHEORY,SUCHASHIDDENMARKOVMODELHMMANDDYNAMICTIMEWARPINGDTWWITHTHEDEEPSTUDYOFSPEECHRECOGNITION,ITISFOUNDTHATSPEECHSIG2FIG1BLOCKDIAGRAMOFSPEECHRECOGNITIONSYSTEM第6期张毅,等改进的智能机器人语音识别方法801HZHNZN3THEORYANDMETHODEXTRACTIONOFSPEAKERINDEPENDENTLNHZ2N1INTRODUCE1INTO2,ANDDERIVEZ1ONBOTHSIDES,2ISCHANGEDINTO3FEATURESFROMTHESPEECHSIGNALISTHEFUNDAMENTALPROBLEMOFSPEAK2ERRECOGNITIONSYSTEMTHESTANDARDMETHODOLOGYFORSOLVINGTHISPROBLEMUSESLINEARPREDICTIVECEPSTRALCOEFFICIENTSLPCCANDMEL2FREQUENCYCEPSTRALCO2EFFICIENTMFCCBOTHTHESEMETHODSARELINEARPRO25LN151HNZN31P5Z5ZN1AZK1KK1EQUATION4ISOBTAINEDPPKNHNZN1KAZK1KK1N1K1HAVEPROPERTIESCAUSEDBYTHEVOCALTRACTRESONANCESTHESEFEATURESFORMTHEBASICSPECTRALSTRUCTUREOFTHESPEECHSIGNALHOWEVER,THENON2LINEARINFORMATIONINSPEECHSIGNALSISNOTEASILYEXTRACTEDBYTHEPRESENTFEATUREEXTRACTIONMETHODOLOGIESSOWEUSEFRACTALDI2MENSIONTOMEASURENON2LINEARSPEECHTURBULENCETHISPAPERINVESTIGATESANDIMPLEMENTSSPEAKERI2DENTIFICATIONSYSTEMUSINGBOTHTRADITIONALLPCCANDNON2LINEARMULTISCALEDFRACTALDIMENSIONFEATUREEXTRACTION31LINEARPREDICTIVECEPSTRALCOEFFICIENTSLINEARPREDICTIONCOEFFICIENTLPCISAPARAME2TERSETWHICHISOBTAINEDWHENWEDOLINEARPREDICTIONANALYSISOFSPEECHITISABOUTSOMECORRELATIONCHARAC2TERISTICSBETWEENADJACENTSPEECHSAMPLESLINEARPRE2DICTIONANALYSISISBASEDONTHEFOLLOWINGBASICCON2CEPTSTHATIS,ASPEECHSAMPLECANBEESTIMATEDAP2PROXIMATELYBYTHELINEARCOMBINATIONOFSOMEPASTSPEECHSAMPLESACCORDINGTOTHEMINIMALSQUARESUMPRINCIPLEOFDIFFERENCEBETWEENREALSPEECHSAMPLEINCERTAINANALYSISFRAMESHORT2TIMEANDPREDICTIVESAMPLE,THEONLYGROUPOFPREDICTIONCOEFFICIENTSCANBEDETERMINEDLPCCOEFFICIENTCANBEUSEDTOESTIMATESPEECHSIGNALCEPSTRUMTHISISASPECIALPROCESSINGMETHODINANALYSISOFSPEECHSIGNALSHORT2TIMECEPSTRUMSYSTEMFUNCTIONOFCHANNELMODELISOBTAINEDBYLINEARPREDIC2TIONANALYSISASFOLLOW4EQUALONBOTHSETCOEFFICIENTSOFEQUALPOWERSTHUSHNSIDESOF4,CANBEOBTAINEDFROMAKHN0A101NNN1KAN15NPAKHNNK1K1N1KAHN1NPKKNK1THECEPSTRUMCOEFFICIENTCALCULATEDINTHEWAYOF5ISCALLEDLPCC,NREPRESENTSLPCCORDERWHENWEEXTRACTLPCCPARAMETERBEFORE,WESHOULDCARRYONSPEECHSIGNALPRE2EMPHASIS,FRAMINGPROCESSING,WINDOWINGPROCESSINGANDENDPOINTSDETEC2TIONETC,SOTHEENDPOINTDETECTIONOFCHINESECOM2MANDWORD“FORWARD”ISSHOWNINFIG2,NEXT,THESPEECHWAVEFORMOFCHINESECOMMANDWORD“FORWARD”ANDLPCCPARAMETERWAVEFORMAFTERENDPOINTDETECTIONISSHOWNINFIG31HZ1P1AKZKK1WHEREPREPRESENTSLINEARPREDICTIONORDER,AK,K1,2,PREPRESENTSPREDICTIONCOEFFICIENT,IMPULSERESPONSEISREPRESENTEDBYHNSUPPOSECEPSTRUMOFTHEN1CANBEEX2HNISREPRESENTEDBYHN,PANDEDAS2FIG2ENDPOINTDETECTIONOFCHINESECOMMANDWORD“FORWARD”FIG3SPEECHWAVEFORMOFCHINESECOMMANDWORD“FORWARD”ANDLPCCPARAMETERWAVEFORMAFTERENDPOINTDETECTION32SPEECHFRACTALDIMENSIONCOMPUTATIONFRACTALDIMENSIONISAQUANTITATIVEVALUEFROMFIG4SPEECHWAVEFORMOFCHINESECOMMANDWORD“FORWARD”ANDFRACTALDIMENSIONWAVEFORMAFTERENDPOINTDETECTIONINGSPEECHSIGNALOFLPCCANDFRACTALDIMENSION,WEMIXBOTHTOBETHEFEATURESIGNAL,THATIS,FRACTALDI2MENSIONDENOTESTHESELF2SIMILARITY,PERIODICITYANDRANDOMNESSOFSPEECHTIMEWAVESHAPE,MEANWHILELPCCFEATUREISGOODFORSPEECHQUALITYANDHIGHONI2DENTIFICATIONRATETHESCALERELATIONONTHEMEANINGOFFRACTAL,ANDALSOAMEASURINGONSELF2SIMILARITYOFITSSTRUCTURETHEFRACTALMEASURINGISFRACTALDIMENSION627FROMTHEVIEW2POINTOFMEASURING,FRACTALDIMENSIONISEXTENDEDFROMINTEGERTOFRACTION,BREAKINGTHELIMITOFTHEGENERALTO2POLOGYSETDIMENSIONBEINGINTEGERFRACTALDIMENSION,FRACTIONMOSTLY,ISDIMENSIONEXTENSIONINEUCLIDEANGEOMETRYTHEREAREMANYDEFINITIONSONFRACTALDIMENSION,EG,SIMILARDIMENSION,HAUSDOFFDIMENSION,INFOR2MATIONDIMENSION,CORRELATIONDIMENSION,CAPABILITYTOANNSNONLINEARITY,SELF2ADAPTABILITY,RO2SELF2LEARNINGSUCHOBVIOUSADVANTAGES,ITSDUEBUSTANDGOODCLASSIFICATIONANDINPUT2OUTPUTREFLECTIONABILITYARESUITABLETORESOLVESPEECHRECOGNITIONPROBLEMDUETOTHENUMBEROFANNINPUTNODESBEINGFIXED,THEREFORETIMEREGULARIZATIONISCARRIEDOUTTOTHEFEATUREPARAMETERBEFOREINPUTTEDTOTHENEURALNETWORK9INOUREXPERIMENTS,LPCCANDFRA