A Decision Theoretic Formulation for Robust Automatic Speech Recognition
CONTENTS +PVTQFWEVKQP
3.3 Adaptive Decision Rules Constructed from Training Samples
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KU ECNNGF VJG Bayes’ risk 6JKU TKUM XCNWG KU VJG DGUV VJCV ECP DG CEJKGXGF KH VJG FKUVTKDWVKQPKU MPQYP
+P URGGEJ TGEQIPKVKQP C TGCUQPCDNG QRVKQP KU VQ CUUWOG VJCV GXGT[ OKUENCUUKſECVKQP QHKU GSWCNN[ UGTKQWU VJGTGD[ TGUWNVKPI KP VJG UQECNNGF0-1 loss function
KH EQTTGEV FGEKUKQP
KH YTQPI FGEKUKQP
HQT Ï Ï 5WDUVKVWVKPI KPVQ YG QDVCKP
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6JGTGHQTG KP VJG ECUG QH VJG NQUU HWPEVKQP VJG VQVCN TKUM KU VJG WPEQPFKVKQPCN GTTQT RTQDCDKNKV[ YJKEJ KU CRRCTGPVN[ C IQQF OGCUWTG QH VJG SWCNKV[ QH FGEKUKQP TWNGU HQT VJG #54 VCUM 6JG QRVKOCN FGEKUKQP TWNG WPFGT VJGminimum classification errorETKVGTKQP YKVJ VJG NQUU HWPEVKQP KU VJGP UQNXGF CU UWEJ VJCV
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YJKEJ KU CNUQ MPQYP CU VJGMAP decision rule
+P UWOOCT[ KP EQPUVTWEVKPI VJGUG QRVKOCN FGEKUKQP TWNGU KV YCU CUUWOGF VJCV EQO RNGVG RTKQT KPHQTOCVKQP CDQWV VJG ENCUUGU KU MPQYP KG
VJG QDUGTXCVKQP URCEG ÜKU IKXGP VJG NQUU HWPEVKQPKU IKXGP CPF
VJG VTWG 2&(QTCPFCTG MPQYP
7PFGT VJGUG CUUWORVKQPU VJG QRVKOCNKV[ ETKVGTKQP KU VJG OKPKOK\CVKQP QH VJG TKUM HWPEVKQPCN CPF VJG QRVKOCN FGEKUKQP TWNG KU VJG $C[GUŏ FGEKUKQP TWNG
3.3 Adaptive Decision Rules Constructed from Training Samples
+P RTCEVKEG YG MPQY PGKVJGT VJGtrueRCTCOGVTKE HQTO QH VJG LQKPV FKUVTKDWVKQP PQT KVUtrueRCTCOGVGTU 9G UJCNN UC[ VJCV YG JCXGprior uncertainty=? KP VJKU ECUG
+H YG JCXG UQOG NCDGNGF independentVTCKPKPI UCORNG UGV
QDVCKPGF D[ C UGTKGU QHindependentGZRGTKOGPVU UWEJ VJCV
HQT VJG #54 VCUM CV JCPF QT KP OKPF YG ECP TGFWEG VJG RTKQT WPEGTVCKPV[ D[
EQPUVTWEVKPI C FGEKUKQP TWNG HTQO 6JG FGEKUKQP TWNGDCUGF QP VJG VTCKPKPI UGVCPF WUGF VQ ENCUUKH[ C TCPFQO QDUGTXCVKQPXVJCV KUindependentQH KU ECNNGF CPadaptive decision rule=? 6JGTG CTG UGXGTCN RTKPEKRNGU VJCV ECP DG WUGF HQT VJG EQPUVTWEVKQP QH UWEJ TWNGU6YQ QH VJGO CTG DTKGƀ[ FKUEWUUGF KP VJG HQNNQYKPI 3.3.1 Plug-in Bayes’ Decision Rules with Maximum-likelihood Density
Estimate
3.3.1.1 What are Plug-in Bayes’ Decision Rules?
6JG OQUV RQRWNCT HCOKN[ QH CFCRVKXG FGEKUKQP TWNGU OKIJV DG VJG UQECNNGFplug-in decision rules (QT VJKU CRRTQCEJ NGV DG CP[ UVCVKUVKECN GUVKOCVQTU QH VTWG FKUVTKDWVKQPUDCUGF QP VJG VTCKPKPI UCORNG 6JGplug-in decision rule=? KU VJG CFCRVKXG FGEKUKQP TWNG
FGTKXGF HTQO VJG $C[GUKCP FGEKUKQP TWNG D[ UWDUVKVWVKQP QH VJG GUVKOCVQTU HQT WPMPQYP
$[ XCT[KPI VJG NQUU HWPEVKQP CPF D[ WUKPI VJG FKHHGTGPV MKPFU QH GUVKOCVQTU C HCKTN[
TKEJ HCOKN[ QH RNWIKP FGEKUKQP TWNGU ECP DG QDVCKPGF (QT GZCORNG CFQRVKPI VJG NQUU HWPEVKQP YKNN NGCF VQ VJG HQNNQYKPI RNWIKP FGEKUKQP TWNG
YJKEJ KU CNUQ MPQYP CU VJGplug-in MAP decision rule
+V ECP DG UJQYP =? VJCV VJG RNWIKP FGEKUKQP TWNG YJKEJ KU CP GUVKOCVG QH VJG VQVCN TKUM WUKPI VJGdensity plug-in estimator
3.3.1.2 Why Could Plug-in Bayes’ Decision Rules Work?
#U PQVGF KP =? VJG RNWIKP TKUM
QH VJG RNWIKP $C[GUŏ FGEKUKQP TWNG KP 'S KU QHVGP NGUU VJCP KVU VQVCN TKUMCPF KU GXGP QRVKOKUVKECNN[ DKCUGF CU CP GUVKOCVQT QH VJG $C[GUŏ TKUM
Property: +H VJG GUVKOCVQTU CTG RQKPVYKUG WPDKCUGF VJGP
*QYGXGT VJG WUGHWNPGUU QH VJG RNWIKP $C[GUŏ FGEKUKQP TWNG KP 'S ECP DG LWUVKſGF D[ VJG HQNNQYKPI VJGQTGO QHBayes’ risk consistency=?
Theorem: (Bayes’ risk consistency): +H VJG GUVKOCVQTU CTG UVTQPIN[
EQPUKUVGPV KG EQPXGTIG VQ VJG VTWG FKUVTKDWVKQPU CNOQUV UWTGN[ CU VJG VTCKPKPI UCORNG UK\GKPETGCUGU VJGP VJG RNWIKP TKUM HQT VJG RNWIKP FGEKUKQP TWNG KP 'S KU C UVTQPIN[
EQPUKUVGPV GUVKOCVQT QH VJG $C[GUŏ TKUM KG
3.3.1.3 Implications on Parametric Models and Parameter Estimation +P RTCEVKEG DGECWUG QH VJG EQPUVTCKPVU QH VJG NKOKVGF EQORWVCVKQPCN TGUQWTEGU CPF VTCKPKPI FCVC YG CNYC[U JCXG VQassumeUQOG RCTCOGVTKE HQTO HQT GI XKCCPF 6JG RCTCOGVGT UGV JCU VQ DGestimatedHTQO VJG IKXGP VTCKPKPI UGV D[ WUKPI EGTVCKP RCTCOGVGT GUVKOCVKQP VGEJPKSWGU 6JG CDQXG
$C[GUŏ TKUM EQPUKUVGPE[ VJGQTGO VGNNU WU VJCV KV KU QHVGP RQUUKDNG VQ EQPUVTWEV RNWIKP RTQEGFWTGU VJCV CTGBayes’ risk consistentKP VJG UGPUG VJCV VJG UGSWGPEG QH RNWIKP TKUMU EQPXGTIGU VQ VJG $C[GUŏ TKUM CU VJG VTCKPKPI UGVU KPETGCUG KP UK\G *QYGXGT VJGTG KU CP KORQTVCPV CUUWORVKQP DGJKPF VJKU CTIWOGPV VJCV KU VJG CUUWOGF FKUVTKDWVKQPU
CPF QDG[ VJG RCTCOGVTKE UVTWEVWTG KP SWGUVKQP +P QTFGT VQ CEJKGXG C IQQF CRRTQZKOCVKQP VQ TGCNKV[ UQOG ƀGZKDNG RCTCOGVTKE OQFGNU UJQWNF DG CFQRVGF
%WTTGPVN[ VJG OQUV YKFGN[ CFQRVGF CPF VJG OQUV UWEEGUUHWN OQFGNKPI CRRTQCEJ VQ
#54 KU VQ WUG C UGV QH JKFFGP /CTMQX OQFGNU *//U CU VJG CEQWUVKE OQFGNU QH UWD YQTF QT YJQNGYQTF WPKVU CPF VQ WUG VJG UVCVKUVKECN ITCO OQFGN QT KVU XCTKCPVU CU NCPIWCIG OQFGNU HQT YQTFU CPFQT YQTF ENCUUGU 6JG TGCFGTU CTG TGHGTTGF VQ IQQF VW VQTKCNU KP = ? CPF =? HQT CP KPVTQFWEVKQP VQ VJG CDQXG CRRTQCEJGU CPF VJGKT CRRNKECVKQPU $[ WUKPI VJG CDQXGOGPVKQPGF RNWIKP /#2 FGEKUKQP TWNG KV JCU DGGP TGRGVKVKXGN[ UJQYP D[ GZRGTKOGPVU KP VJG RCUV VJTGG FGECFGU VJCV IKXGP C NCTIG COQWPV QHrepresentativeVTCKPKPI URGGEJ CPF VGZV FCVC IQQF UVCVKUVKECN OQFGNU QH URGGEJ CPF NCPIWCIG ECP DG EQPUVTWEVGF VQ CEJKGXG C JKIJ RGTHQTOCPEG HQT C YKFG TCPIG QH #54 VCUMU 6JKU JCU IKXGP VJG URGGEJ TGUGCTEJ EQOOWPKV[ C EGTVCKP NGXGN QH EQPſFGPEG KP
DGNKGXKPI VJCV VJGDiscrete HMM&*// =? CPF VJG /KZVWTG )CWUUKCP Contin-uous Density HMM%&*// = ? VQIGVJGT YKVJITCO OQFGNU =?
RTQXKFG C IQQF CRRTQZKOCVG RCTCOGVTKE HQTO HQT CPF TGURGE VKXGN[ #NVJQWIJ VJGUG OQFGNU CTG CRRCTGPVN[ KORGTHGEV = ? VJG[ CTG OCVJGOCVKECNN[ YGNNFGſPGF CPF ECRCDNG QH UKOWNVCPGQWUN[ OQFGNKPI DQVJ VJG URGE VTCN CPF VGORQTCN XCTKCVKQP KP URGGEJ 6JG[ CTG CNUQ YGNN VJQWIJV QH DGECWUG VJG[ DQVJ ſV KPVQ VJG HTCOGYQTM QHfinite stateTGRTGUGPVCVKQPU = ? QHknowledge sourcesUQ VJCV VJG URGGEJ TGEQIPKVKQP RTQDNGO ECP DG UQNXGF CU Cnetwork searchRTQDNGO QXGT C EQORNGZ PGVYQTM TGRTGUGPVCVKQP QH URGGEJ CPF NCPIWCIG =? $CUGF QP VJG DG NKGH VJCV VJGUG CEQWUVKE CPF NCPIWCIG OQFGNU CTG IQQF CRRTQZKOCVGU VJGmaximum likelihood/. GUVKOCVG HQT VJG *// RCTCOGVGTU = ? CPF ITCO OQFGN RCTCOGVGTU = ? JCU DGGP VJG OQUV RQRWNCT RCTCOGVGT GUVKOCVKQP OGVJQF 6JG YKFGURTGCF WUG QH VJG RNWIKP /#2 FGEKUKQP TWNG YKVJ VJG /. GUVKOC VQT ECP DG LWUVKſGF D[ WUKPI VJG CDQXG $C[GUŏ TKUM EQPUKUVGPE[ VJGQTGO FWG VQ VJG HQNNQYKPI HCEVU
6JG /. GUVKOCVQTU QHCTG UVTQPIN[ EQPUKUVGPV WPDKCUGF CPF GHſEKGPV 6JKU ECP VJGP DG VTCPUNCVGF KPVQ VJG FKUVTKDWVKQP EQPUKUVGPE[ KH VJG RCTCOGVTKE
HQTOU QH VJG
CPF
CTG KPFGGF EQTTGEV
#EEQTFKPI VQ QWT MPQYNGFIG KV YCU 0CFCU =? YJQ ſTUV RTQXKFGF UWEJ CP KPUKIJV HQT VJG URGGEJ TGEQIPKVKQP EQOOWPKV[
1H EQWTUG QPG ECP CNYC[U CTIWG VJCV CNVJQWIJ VJG /. GUVKOCVQTU CPFOC[ DG GZEGNNGPV GUVKOCVQTU QHCPF VJGTG KU PQ IWCTCPVGG VJCV
CPF
CTG IQQF IWGUUGU HQTCPFDGECWUG QH VJG KPEQTTGEV OQFGN CUUWORVKQPU 0QT KU
PGEGUUCTKN[ C IQQF CRRTQZKOCVKQP VQ
6JG RGTHQTOCPEG QH VJG RNWI KP TWNGU CPF QVJGT RTQEGFWTGU UJQWNF TGCNN[ DG LWFIGF D[ VJG ETKVGTKQP QH VQVCN TKUM QT D[ QVJGT ETKVGTKC VKGF OQTG FKTGEVN[ VQ VJG ENCUUKſECVKQP CEEWTCE[ VJCP VQ VJG DGJCXKQT QH CU Cpoint estimatorHQT 6JKU JCU OQVKXCVGF OCP[ UVWFKGU KP VJG RCUV VYQ FGECFGU CKOKPI CV C IQQF CNVGTPCVKXG VQ /. VTCKPKPI 1PG OGVJQF KUminimum discrimination information/&+ VTCKPKPI =? YJKEJ CFLWUVU VJG *// RCTCOGVGTU VQ OKPKOK\G VJGdiscrimination information QTdirected divergence DGVYGGP VJG CU UWOGF *// FKUVTKDWVKQP CPF VJG DGUV RQUUKDNG FKUVTKDWVKQP FGTKXGF HTQO VJG VTCKPKPI FCVC WPFGT EGTVCKP EQPUVTCKPVU GODGFFGF KP VJG VTCKPKPI FCVC 7PHQTVWPCVGN[ PQ UKI PKſECPV GZRGTKOGPVCN TGUWNVU JCXG DGGP TGRQTVGF VQ UJQY JQY /&+ YQTMU KP C URGGEJ TGEQIPKVKQP VCUM #PQVJGT ENCUU QH CRRTQCEJGU KU VJG UQECNNGFdiscriminative training OGVJQF 5QOG QH VJGO UWEJ CUmaximum mutual information//+ VTCKPKPI =?
conditional maximum likelihood estimate%/.' =? CPFH-criteria=? CKO KP FKTGEVN[ CV TGFWEKPI VJG GTTQT TCVG QH VJG URGGEJ TGEQIPK\GT QP VJG VTCKPKPI UGV 1VJGT OGVJQFU UWEJ CUcorrective training=? CPFminimum empirical classification error VTCKPKPI = ? VT[ VQ TGFWEG VJG TGEQIPKVKQP GTTQT TCVG QP VTCKPKPI UCORNG UGV KP C OQTG FKTGEV YC[ #OQPI VJGUG CRRTQCEJGU VJG OKPKOWO GORKTKECN ENCUUKſ ECVKQP GTTQT MPQYP CU /%' HQTOWNCVKQP RTQRQUGF KP =? KU KP O[ QRKPKQP OQTG VJGQTGVKECNN[ UQWPF VJWU YKNN DG FKUEWUUGF DTKGƀ[ KP VJG HQNNQYKPI
3.3.2 Maximum-Discriminant Decision Rules Minimizing the Empiri-cal Classification Error
3.3.2.1 What are Maximum-Discriminant Decision Rules?
5WRRQUG QPG ECP FGſPG Cdiscriminant function£ HQT GCEJ ENCUU VJCV EJCTCEVGTK\GU VJG UKOKNCTKV[ DGVYGGP CP QDUGTXCVKQPCPF VJG ENCUU YJGTGKU VJG UGV QH ENCUUKſGT RCTCOGVGTU VQ DG GUVKOCVGF HTQO VJG VTCKPKPI FCVC UGV 0CVWTCNN[
VJG HQNNQYKPImaximum-discriminant decision rule
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ECP DG WUGF VQ ENCUUKH[ CP WPMPQYP QDUGTXCVKQPKPVQ QPG QH VJGENCUUGU KPÏ 6JG QDXKQWU ETKVGTKQP HQT GUVKOCVKPI VJG ENCUUKſGT RCTCOGVGTU KU VQ OKPKOK\G VJG GORKTKECN ENCUUKſECVKQP GTTQT QP VJG VTCKPKPI UCORNG UGVFGſPGF CU HQNNQYU
PWODGT QH EQTTGEV ENCUUKſECVKQPU D[
VQVCN PWODGT QH UCORNG QDUGTXCVKQPU QP 0QY NGVFGPQVG CP CTDKVTCT[ DWV EQORNGVGN[ URGEKſGF EQNNGEVKQP QH FKUETKOKPCPV DCUGF FGEKUKQP TWNGU # UCORNGDCUGF FKUETKOKPCPV FGEKUKQP TWNG YKNN DG ECNNGF Cminimum misclassificationQTbest-countFKUETKOKPCPV FGEKUKQP TWNG KH KV OKP KOK\GU VJG UCORNG GTTQT TCVG COQPI CNN FKUETKOKPCPV FGEKUKQP TWNGU VJCV KU C DGUVEQWPV FKUETKOKPCPV FGEKUKQP TWNG UCVKUſGU
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5KOKNCT VQ VJG ECUG QH VJGdensity estimator KV ECP DG UJQYP =? VJCV
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5Q KU CP QRVKOKUVKECNN[ DKCUGF GUVKOCVQT QH VJG CEVWCN GTTQT TCVG QH CPF VJG NGCUV RQUUKDNG GTTQT TCVG
3.3.2.2 Why Could Discriminant Approach Work?
6JG WUGHWNPGUU QH VJG DGUVEQWPV FKUETKOKPCPV CRRTQCEJ ECP DG LWUVKſGF D[ VJG HQN NQYKPI VJGQTGO UKOKNCT VQ VJG QPG KP UGEVKQP CPF CNUQ RTQXGF D[ )NKEM =?
Theorem: (Uniform Convergence) # FKUETKOKPCPV FGEKUKQP TWNG YKNN DG ECNNGF m-convexKH KVU RCTVKVKQP TGIKQPU CTG UGVU KP VJG ſPKVG ſGNF IGPGTCVGF D[ UQOG OGCUWTCDNG EQPXGZ UGVU #U VJG UCORNG UK\G VJG GUVKOCVQT EQPXGTIGU VQ¼½
uniformlyQXGT CNN FKUETKOKPCPV FGEKUKQP TWNGU KP CP[ EQNNGEVKQP QHEQPXGZ FKUETKOKPCPV FGEKUKQP TWNGU VJCV KU VJG EQP XGTIGPEG KU CNOQUV UWTGN[ CU
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6JKU WPKHQTO EQPXGTIGPEG KORNKGU VJCV VJG best-countFKUETKOKPCPV KU CU[ORVQVKECNN[ QRVKOCN KP VJG UGPUG QH
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YKVJ RTQDCDKNKV[ QPG CPF
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YKVJ RTQDCDKNKV[ QPG +H EQNNGEVKQPEQPVCKPU CP[ QRVKOCN FKUETKOKPCPV FGEKUKQP TWNG
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VJGP KU CU[ORVQVKECNN[ QRVKOCN KP VJG WPTGUVTKEVGF UGPUG XK\ UVTQPIN[ EQPUKU VGPV KP $C[GUŏ TKUM #U RQKPVGF QWV KP =? VJKU TGUWNV KU PCTTQYGT VJCP KVU RCTCNNGN TGUWNV HQT FGPUKV[ GUVKOCVGU UVCVGF KP VJG VJGQTGO KP 5GEVKQP +V YKNN DG KPVGT GUVKPI VQ KPXGUVKICVG JQY HCT VJG CDQXG TGUWNV ECP DG IGPGTCNK\GF VQ C YKFGT TCPIG QH FKUETKOKPCPV HWPEVKQPU
3.3.2.3 Implications on the Choice of Discriminant Functions and the Practical Training Algorithms
6JG CDQXG VJGQTGVKECN TGUWNV IKXGU QPG EQPſFGPEG VJCV KH C RTQRGT HQTO HQT VJG FKU ETKOKPCPV HWPEVKQPU ECP DG URGEKſGF HQT VJG IKXGP RCVVGTP TGEQIPKVKQP RTQDNGO KV KU QHVGP RQUUKDNG VQ EQPUVTWEV OCZKOWOFKUETKOKPCPV FGEKUKQP TWNGU D[ GUVKOCVKPI VJG ENCUUKſGT RCTCOGVGTU WPFGT VJG ETKVGTKQP QH OKPKOWO GORKTKECN ENCUUKſECVKQP GTTQT 5WEJ FGEKUKQP TWNGU CTG $C[GUŏ TKUM EQPUKUVGPV KP VJG UGPUG VJCV VJG UGSWGPEG QH GO RKTKECN TKUMU EQPXGTIGU VQ VJG $C[GUŏ TKUM CU VJG VTCKPKPI UGVU KPETGCUG KP UK\G 1H EQWTUG JQY VQ FGſPG CP QRVKOCN HQTO HQT VJG FKUETKOKPCPV HWPEVKQPU KU CRRNKECVKQP FGRGPFGPV CPF TGOCKPU NCTIGN[ CP QRGP TGUGCTEJ RTQDNGO 1P VJG QVJGT JCPF VJG IQQF PGYU KU VJCV VJG UOQQVJ /%' QDLGEVKXG HWPEVKQP RTQRQUGF KP =? ECP CRRTQZK OCVG VJG GORKTKECN GTTQT TCVG HQT VJG FGUKIP UCORNG UGV CTDKVTCTKN[ ENQUGN[ +V ECP VJWU DG WUGF CU VJG FGUKIP ETKVGTKQP VQ DG QRVKOK\GF D[ CP[ ITCFKGPVDCUGF QRVKOK\CVKQP OGVJQFU +P VJG RCUV FGECFG VJKU /%' HQTOWNCVKQP JCU DGGP GZVGPUKXGN[ UVWFKGF TGſPGF CPF UWEEGUUHWNN[ CRRNKGF VQ UQNXKPI OCP[ RCVVGTP TGEQIPKVKQP CRRNKECVKQPU UGG HQT GZCORNG = ? CPF VJG TGHGTGPEGU VJGTGKP
3.3.3 Discussion
5Q HCT YG JCXG EQPUKFGTGF VJG HQNNQYKPI VYQ UVTCVGIKGU VJCV JCXG DGGP WUGF VQ EQP UVTWEV C OQFGTP #54 U[UVGO
7UKPIplug-in MAPCU C FGEKUKQP TWNG HQT TGEQIPKVKQP FGEKUKQP CPF /. CU C ETKVGTKQP HQT VJG GUVKOCVKQP QH FGEKUKQP RCTCOGVGTU
7UKPImaximum discriminantCU C FGEKUKQP TWNG HQT TGEQIPKVKQP FGEKUKQP CPF minimum empirical classification error/%' CU C ETKVGTKQP HQT VJG GUVKOCVKQP QH FGEKUKQP RCTCOGVGTU
6JG HQNNQYKPI EQPENWUKQPU OC[ DG FTCYP EQPEGTPKPI VJGUG VYQ UVTCVGIKGU
6JG CU[ORVQVKE DGJCXKQT QH VJG ſTUV CRRTQCEJ YKNN FGRGPF QP VJG CRRTQRTK CVGPGUU KP VJG UGPUG QH GUVKOCVQT EQPUKUVGPE[ QH VJG RCTCOGVTKE HQTOU QH VJG CUUWOGF FKUVTKDWVKQPU
6JG CU[ORVQVKE DGJCXKQT QH VJG UGEQPF CRRTQCEJ YKNN FGRGPF QP VJG EJQKEG QH VJG FKUETKOKPCPV HWPEVKQP
6JGQTGVKECNN[ URGCMKPI KV KU PQV UQ ENGCT [GV YJKEJ UVTCVGI[ KU DGVVGT HQT C OQFGT CVGN[ UK\GF VTCKPKPI UGV *QYGXGT KP VJG RCUV FGECFG KV JCU DGGP FGOQPUVTCVGF D[
OCP[ TGUGCTEJ ITQWRU VJCV YJGP UWHſEKGPV COQWPV QHrepresentativeVTCKPKPI FCVC CTG CXCKNCDNG CP #54 U[UVGO EQPUVTWEVGF WPFGT VJG UGEQPF RTKPEKRNG ECP QWVRGTHQTO KVU EQWPVGTRCTV EQPUVTWEVGF WPFGT VJG ſTUV RTKPEKRNG HQT OCP[ #54 CRRNKECVKQPU