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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/lawn_bmvc94.pdf9 Aug 2005: "!# $%& %'()#,&-##)./10324 56&-786&9: ;#<: =;#: >?@-<;A66"B9: ;#<: =;#: >? C<DFEGHIJBGKMLONQPLSRLTU(VQLP LXWZYQ[&R]ARR[&T_T_UVALO]QRLOBaNFLPSL=NQU ]bWBcdP ]QNA[eTAdfUf[&ghNQPBiLRU P]QSU]QP LjWBTQYhLdghU[&ThPLBiLP kXWlc&dP -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/yow_accv95.pdf9 Aug 2005: "!#$%&"'(%&). ,-/.1023435.67398 :9.<;>=3@?<ACBDE3 0-/FG3@HIHJ:KLACFM:NBDEOAC.PD3NQR.6@-I.ACASB-I.6T.-IU4ACBWVX-/DXYZ3NQ[0:9O?B-];P65A. 0:NO?B-J;65AZ0_[ aSbdce<R.6@H]:9.<;. fghji9k9lm5in&o9prqPs5tus5vwxCvXqzy -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/logan_sst96.pdf9 Aug 2005: #"$%&')(,-.%%&( 0/-#1""324%5/246"78&9:5. ;<8<'7>=@?9A@BA@BDCE<8F@<'8=HGIBDJK=B&L>MNMNO>PQ'/I JRI =BSA@BDCT8=G>=UVI ONJW(XY=ZL@Q. "M>L[AXYU]M>BDU=>_8B?IBDMNM>XaIBD?HQ.BI bDM>XcJ4I Ued=>_#'A@]G[XaI C?@M. ;'&'f2#PI JL[A@L[M>XCMNJKO>XgI,G[MNJABDMNhA@i? @ -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/james_icassp94.pdf9 Aug 2005: b)yogP_SYjEogZEV=FPWnco,o4o%PSpNV]acV=V]ZRoMLV j@oMRQ6NHTVV=b)oP_SRaRKP_aOaQTLYVqtKZnSaQTLYV )@=KgZNVOSV]¢HZEV=SKWog6To,rsVqtT<j Ë!TtqÍFNnKZnS. ... O<_OJKMLONHPRQTSUWVX[ZHQ6MV]P_QTacb dfZEUgQ6ZEV=V]PRQ6ZEU hiV]jnKP_aRLYV]ZHaâk8VrHZQTrKgV]j@oP_acDM« -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/ellis_tr122.pdf9 Aug 2005: 1(LQ34?OP(0EL>4G7:=Gs71b<7 3F >!=7}LK0H7 P V g+NMI. ... Gs7bO7I3 Pq13457;<)6dI1L13C?OP().d ).k>47PD6>CL!1S>-bcPÁ>Cm7A347o-L>v)>7 |m7I3471347;<7 34?¤7! -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/freitas_saiee.pdf9 Aug 2005: õ èRéüËàbé_ßAâmÛ;âmÛ;òP @.&! / # # $#wRêèRäiÑ,4,Û;èPÑ@Ð,ð;ðTÑ 437 ;Ð0PÐ.22,;Ñ. mÐ41 3 ' ébÜwâmÞßAÛT0> 8ÂâmÛ;òRâmÛ;òýôå,ð8ÜwébðäßáÛ;ÜwëVíèRé4éÜwòRéÜwëiæëbâmèRÛT8ãwäZßáëbëbâ Pã -
A Comparative Study of Methods forPhonetic Decision-Tree State…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_euro97.pdf9 Aug 2005: Thus, although the baseline systems were op-timised over a limited range of thresholds, the figures obtainedare still susceptible to some degree of noise. -
A NEURAL NETWORK BASED, SPEAKER INDEPENDENT, LARGE…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/wernicke_eurospeech93.pdf9 Aug 2005: 1.3. ComputingThe HMM-ANN approach is very computationally expensive.The networks currently require about 1013 floating point op-erations to train and future estimates of the required computepower is one -
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mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/robinson_icassp92.pdf9 Aug 2005: l>SZ'C< 3)OP@j- /);@)f;@!;@j7r) V h[H&M5@)(Af)(>-@ 5 I "HI@EHH)&O})(47)7O;<<?@)(9jAf @@Af3)VX C 3 I"j#f@B < f@C# ... Hl@H-)( (B )(.>#B 4P47(#;47(,4> '6:>o6; H(# q>9Km>o. @#>19I. #AY[>oP>o6;-<V<>_$@E3SRTA6CSÇ4(-Vdf)(T3)&H-mKMC (-"!IC5O})(/H-)( &)(&J! -
Learning New Articulator Trajectories for a Speech Production…
mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/blackburn_icnn95.pdf9 Aug 2005: This minimal amount of parameter optimisation per iteration was necessary to ensure smooth convergence to an op-timal set of input values, and the MSE decreased during training from 1.5007
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