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  1. Results that match 1 of 2 words

  2. 9 Aug 2005: #"%$%#&$%' (. )-,-.0//13254760-8:9<;>=@?A?B1(CDEGFH I!' DEKJ-L M. N /PORQHSR13QUT7VW@W@X. Y 259AZ@/PQ[H9A]T=_a8b1@cORQU9A4Rd@/e /P;R1@QU]fc+/P2@]G=_aga25d39<25//PQ9<25d. h QiSRcj;59B25d@]H=k2l5]mQU//]8b1@cORQU9A4Rd@/8bnGoVpaq. ga25d@?B1@254.
  3. 9 Aug 2005: T"%H7LO$5"%"% 4 $4 5 SP "%5T"<5 2 &<&%%-M,4V4M '5>20 "%L) O-M>. ... 5"%H8>5/ PJHS 5$ 2 5>5 2 >H!"%T $4"<$&% 2 &< T"< 5 2 "%9J >"< 2 45"%(5'5 20 "%L) 0D.
  4. 9 Aug 2005: 5 10 15 20 25 30. 0. 0.1. 0.2. 0.3. 0.4. ... 5 10 15 20 25 30. ZR66 A+ AN. ZR66 A+ 'IE N.
  5. 9 Aug 2005: 600. |700. |800. |900. |1000. |0. |10. |20. |30. |40. |50. ... 20#3O jOj_1 )0 Do"09FO r(_.J#8 GJ_ >> 0OTCj_1K z =C 329F 03TCO&.2 = ( J!u?
  6. 9 Aug 2005: ö. ÚWÖhÖhÓß"â¥Ì"ÐÔÛËWÑ ' '! -0- 20 2. %(ö. 0! âxÑgÐ 0 2 0ðÏÐ7Û9Ó â SÜãCÖhÓÏËWßãÓ ÖhÏ Û9É3Ë'Õ/ gÚWÖhËÚWâËÌFÛÑZÖ7ã%ÐyÕ"Ð7âÓçÖ7ã%ÚÐeÏ%õËÓÐ7Ñå
  7. 9 Aug 2005: "#$% "'&($). ( (, -'./02143$57698: ,;<0>=@?'AB-C.D3$E26FHGJI4KMLNPORQ' JNSI UTMLWV(XZY[[. ] A 1@=4_$acbdBd e. f 3hgji.026A -Sk4502/ -'.1@0>=mlJn5A 0>57-'-'.025A!o-'p3$.=@g!-'5=q. g!p70>57AB=r 5!s=.@-'-'=f 3$gji7.026AB- fut _JbvPw. n5A Ex3$56.
  8. 9 Aug 2005: " #. $%&(' )",-. /103254 ,6, /,87 0/&9,:0; <6&9 ,"0=,?>:0 /0$%@&A'6 )B$DCE#F!G. 4 ,6H,). 4 &F3IJK L@ M% FN,6 O!&PQO <R. SUTFVXW5T(YZD[%F[]Z_(aBbc[edef]Yg_Ah?V[]iFfB[jS?kBkBh9_S9deTlfm_. jFVFVjF[YS?iFjAYgd_(ionApF[]h. dBp5[joqsrtYViFr%[]ioWuqfBhwvx! "
  9. USE OF GAUSSIAN SELECTION IN LARGE VOCABULARY CONTINUOUSSPEECH…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/knill_icslp96.pdf
    9 Aug 2005: 3.2. Gaussian To Cluster Assignment. 10 15 20 25 30 35 40 45 50 5512. ... 1.9 3 - - 20.3 12.881.9 4 - - 24.3 12.46.
  10. 9 Aug 2005: yr= y[P. yÒ0.2. 0.1. 00.1. 0.2. 0.20.1. 00.1. 0.2. 0. 0.5. ... S=5.0. S=20.0. No. rmal. ised. Bas. is L. ikel. iho. od.
  11. 9 Aug 2005: "# $ # # % &!' () % (" (, -). 0/213/54568797:6<;=;>6<?A@CBD/E.0/GFIH9HKJMLONQPRJSDTVUXWZY[]Z_QXacbZ[OdQKYef[hgjiIk!bZ_Q[bZlKY[bZ_nmGKopTVYgqUrlb3gs YftMUuoM[bZ_VgqvwbIxygqYK<g{zRSDTQU|WZYf[O]Z_wXSD}9paA. XZK0>lKnqEK¡!
  12. 9 Aug 2005: 60. 50. 40. 30. 20. 10. 0. 10. 20. 30. iterations number.
  13. 9 Aug 2005: θ /- 200mm+/- 200mm. /- 200mm. o. o. /- 20. /- 40.
  14. 9 Aug 2005: x 10 6. 10. 20. 30. 40. 50. 60. 70. 80.
  15. 9 Aug 2005: 20. 40. 60. 80. 100. 120. 140. 160. vector angle theta (degs). ... 150 -100 -50 0 50 100 1500. 20. 40. 60. 80.
  16. 9 Aug 2005: 0. 20. 40. 60. 80. 100. 120. 140. 160. 180. 200.
  17. 9 Aug 2005: #"$ %&('),-. /1032!054507698;:<:>=@?3A07BC05DFEHGJILKM?7N;O3P,BC05QRETS78VUWUHNVKX Y[Z]<_@ab_ cWdeRfJabfJg ih7j3kFlmnZ]oqpVhngo]osrtd>jvug wyx;Zzp;{@fFu|}C FjVR. MV@tMt>vM tMbJ fJvgbdwfJtd_cn{Z]nfJf<Z]V{VjvRnZ]Mf<abZd_ cugw(xVZqpV{f@jvugwyx;ZqpV{@fFu
  18. 9 Aug 2005: $#%&('!)( ,-)/.-01"20. 3". &)45.7689&:,. ; $.=<>5>?@BAC$.D(E,F!G 3"7. &$4B)9&:,IH ; $.J. 9,D!)#K)FE"&&9('K. ... 2. # ; C " $"!#$C"%. 0 5 10 15 20 25 30 35 40 45 5068.8.
  19. 9 Aug 2005: 1? 21K%aO? 1' 019W?-:( &6-8. 12'? U &/-Y21K%-%20'DO 2'&/?-sM 0'K:G/GJW"!8&/-%,0'?;,!;?%(&WD(-% 2IM? ... 21"21 &/ 0hO -5G/D(;2'?$K%&/K%G/A05O &/ -<20'DO1K% 0 M &621KY0'?8,2''!
  20. 9 Aug 2005: " $#%& " ' )(. # ,-. /. 0214365785:9<;1>=?=?@A9B@. " DC E&F'GHE&IIF. 0J78KLKM@ANLOQPROS7T1>5UKLV:W X<7YOLOS@[Z<O1OS]:@_257Y>@[NKL7TOa<1Bb;cPdXeWN7?Z:9B@bf1BNgOQ]:@hZ:@A9BNL@A@1Bb021jikO1BNg1Bbl+]78=T1>KM1Bm ]na. "!#$!&%(')!,-"./&!%1032546%879')7%1":;
  21. 9 Aug 2005: 0.68. 0.7. 0.72. 0.74. 0.76. 0.78. 0.8. 0.82. 0.84. 1 5 10 15 20 25 30 35 40 45 49. ... 0. 10000. 20000. 30000. 40000. 50000. 60000. 70000. 1 5 10 15 20 25 30 35 40 45 49.
  22. 8 Aug 2005: This task,however, canbe seenas a detectionproblem, and thus in [19, 20] it is argued that the tracking of complexobjectsshouldinvolve theclosesynthesisof objectdetectionandtracking. ... In Proc. 9th Int. Conf. on ComputerVision, volume II,
  23. 9 Aug 2005: #" %$&'()&$%,,-./(0,11'23 ,11'546#78:9<; => ;@?A B # "DC"$E"0%F GH IJ ;K 0=/#MLNC<OPQDCI# = "F. RTSVUXWZY[P]_MUX<YU_]_acbdUXZU_egfha6fH<YWNUia!Yjk]lWU_]lmonpUiaZqrlY ]VstjXuwvxSlZyfMWzY ]{6e|UisyfHW. }H/hiH@/K>/PZXK:0V/@¡ ¢08¢¡
  24. 9 Aug 2005: 20. 0. 20. 40. 60. 80. 100. 120. 140. Strength 85.92. ... 20. 0. 20. 40. 60. 80. 100. 120. 140. Strength 549.2.
  25. 9 Aug 2005: 20 D$qh/% /% ( C $&H 4z{ $9 F $&HV $$ 4|J(! ... f. } ( } a v. 6. f JX 379 "? N/% $R/%$J 1 20 $ $< /%- #" U-, $< O $&H 4s4:.
  26. Parcel:feature subset selectionin variable cost domains M.J.J. Scott, …

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/Scott_tr323.pdf
    9 Aug 2005: 4.1 Introduction. 19. 4.2 The receiver operating characteristic. 20. 4.2.1 Neyman Pearson criterion. ... 20. 4.2 An example of a Neyman Pearson criterion. A maximum false-positive rate ofis set, describing a vertical line in.
  27. 9 Aug 2005: 0 10 20 30 40 50 60 70 80 90 10020.
  28. 9 Aug 2005: "!$##&%' (" '), -(." /0 213"45" 67"895;:77 89<'="'<>?3< @A B.C 27DE 5@ A"6&8 F3=GH@ ="'9 767<>B. IKJLJNMPO2QR&S0O$TVUWTVXZY[O$]IM$_VaLbGRdceS0TgfhO$cHijRkMmlnab0opTqO$UL_. rts&uhv&wxtuyuzs&{-v}|t{-vwas&;w0rts}"s&|V 2- 27V72 j" ¡ ¢$£.
  29. 9 Aug 2005: "$#% & '#($#) ,. -. /)021436587:9;<=1?>A@B@DCEF<HG/%<JI(KLNMPO%QSRTQVUWKWXYE(LZ<H3[W]_acbedf_aghjilk=inmpoeqDrts=iluTmpinv2w2iyxzoeil{}|Win2Ji}qDrtixpmVraxi}mpr urax 2i'mpqDix 2m4ux2krtrupr{x2mW{}|Jw22w2mzrtu2 mWx2i}{oe{ x2rqD{x(umu2ux2kJqDrpiyxkJr
  30. CAMBRIDGE UNIVERSITYENGINEERING DEPARTMENT ����������� ���…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/gsmith_tech_tr345.pdf
    9 Aug 2005: CAMBRIDGE UNIVERSITYENGINEERING DEPARTMENT! " #$!%&'$ ($)", -$. &$/$01'.,,". #,$. 2&3#465798;:=<>?3@BADCFEG79H?IJCKHL<M>GN%OP7RQTSFHMUJSVHWXYZ [M]_abOP]cZ O2)MdDA&egfVh6i. 3PjlkFjGIJ8monl>monFnKn. <XSV50ElQT7qprkFsPYPHl7RtFsoQTI;798buBZ
  31. 9 Aug 2005: 20. 15. 10. 5. 0. 5. 10Initial projection step. first eigenvector depending on τ. ... 20. 15. 10. 5. 0. 5. 10Initial configuration. first eigenvector depending on τ.
  32. 9 Aug 2005: 3BiBGf= AB4 SUT 4/26Ba. X. 0 0.05 0.1 0.15 0.20.75. 0.76. ... SNR. ). theoretical. least squaresfit slope = 0.43. n high_reslog( ). /?4&#&( ; 49 6/?0#&)-7;#9(. 0 0.05 0.1 0.15 0.20.76. 0.78. 0.8.
  33. 9 Aug 2005: C h0. V. 0 5 10 15 20 25 30 35 400. ... 20+ -(x3)/3200e. spline from improved windows. STANDARD WINDOWS. window 4. IMPROVED WINDOWS.
  34. Named Entity Recognition from Speechand Its Use in the ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kim_thesis.pdf
    9 Aug 2005: 2.2). Chapter 2: Previous work Page 20. The a priori probability of the word sequence, the denominator in equation 2.2, is constant for. ... tion [19, 20]. In FASTUS, sentences are processed by a cascaded, nondeterministic finite-state.
  35. Effective Corner Matching P. Smith�, D. Sinclair �, R. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/smith_bmvc1998.pdf
    9 Aug 2005: 20. 40. 60. 80. 100. Integer correct. Integer mismatched. Matched, no match (both). ... This is one source of. British Machine Vision Conference 5. 0 20 40 600.
  36. 9 Aug 2005: CAMBRIDGE UNIVERSITYENGINEERING DEPARTMENT. "!#$&%$'() ,-! /0-1"),-2 3# 4(57698 :<;-6>=;?A@B7CED>FHGI?J=KBMLONPBQLBQLR:SUT8#;; )#!VR%$W2%X4(VR,YHZ[]. GI_O7;ab;2=c d<Dc2e7eQf. g BMab>=W?h:<i7;UjILR?A7;=KCP?A@lknmoL>i7?JL>;;2=W?JL>iqp;rBM=W@Wa";Ls@t+=u
  37. 9 Aug 2005: ¥ ne nm8d0e)q+_Ya#d &¢F. £(£. ,. 0 1 2 30. 20.
  38. 9 Aug 2005: 2.3.5. Deleted interpolation 20. 2.3.6. Modified absolute discounting 21. 2.4. N-gram models 22. ... $'&! (20). Now, from equations (18) and (19):. =! so that, using (20) it follows that: =! and finally substitution of this result into (17) leads us to:.
  39. 9 Aug 2005: 4000. 6000. 0.10 0.20 0.30 0.40! "$#. 0. 500. 1000. 1500.
  40. 9 Aug 2005: CAMBRIDGE UNIVERSITYENGINEERING DEPARTMENT! "#$ %& ' ( ) , -./0 ". 13254687289:7;=<:>3?A@8BDCE;F9G>IHKJL>MHN@O,P 4 77>IHQ6 O%R,SMTQU 7A<. WVX "YZ[ ,]VX D_QaC SKb 7cedQ7[9,f(gh@f[i3iMg. j >Iced89Z;k6hl37nmEHX; b 79G?L;=<porqsH8l3;FH877[9Z;FH8l R
  41. 9 Aug 2005: Deco and Obradovic, 1996, p. 20). We shall use this fact in Section 2.1.5below.
  42. 9 Aug 2005: "#$#%'&((). -,. /,10%,32465879;:=<>,? ,3@-A3A3BC5D46EFB HGI;JKC&MLNI&( OKPQCRSQ. 0F7TCUWVX46UWY[Z]_? (4bacTCUWd8BCe7(f;ECd8g7UW9hdjikYIl>ECedjEF77UWd8ECem7nX46UWiha7E3iopUhVCanFdjEFeiWAEq3iWUh77i?(46acTFUWd8BCe7;?%r(sIZ]<Mt. l>ECe5D46ECB. lua
  43. A NEURAL NETWORK BASED, SPEAKER INDEPENDENT, LARGE…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/wernicke_eurospeech93.pdf
    9 Aug 2005: The front endoptions are:. MEL+ a 20 channel mel-scaled filter bank with voicing fea-tures. ... Pre- Error Rate %Net Processing feb89 oct89 feb91 sep92. RNN MEL+ 16/32 4.8% 6.1% 5.4% 10.7%RNN MFCC 10/20 6.1% 7.6% 7.4%
  44. 9 Aug 2005: D,?A<D)20/>; )87 ;=@ 20/(4KB 5= @87.2,B 5=- ; <D) E? )873?
  45. 9 Aug 2005: Ã. 0.0%. 5.0%. 10.0%. 15.0%. 20.0%. 25.0%. Speakers. % W. ord.
  46. 9 Aug 2005: #"$ %&%. ')(),.-0/,132145467 8:9;(1,<>=@? A3B:CED5FHGIFKJELMDNFHDPOQBMD5RSUTVLWBMCEDXRDPY0LMDPDZT5S[ TQ]BMT0LT5S_aCEGcbdTeFfTVJECQg. GdOh bcD5iBMLMGIPjkb)j5OlR h bcDN]BML:TVOEGI h OEYVGdOEDPDPL:GcOEY. j5BB:CEDm
  47. 9 Aug 2005: 0. 0.05. 0.1. 0.15. 0.2. 0.25. 0.3. 0.35. 0.4. -20 -15 -10 -5 0 5 10 15 20.
  48. ijrr.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/cipolla_IJRR97.pdf
    9 Aug 2005: " #%$&'()#%, )'. -/.,0213546.879;:2.,<=<?>A@>CBEDGFHB2D 361IKJL<?>NM1NOPQ>N3SRSTQUV WXUYV,VZ. [,]&_baScedbfhgij&iNkmlonbkpg?qml?fsrNkg2jtYu&v6kmwyxkz;dYuSnz?u{xkj5g;|bl}k2z;dYkenfsykml}kmtz;fhj!wAfstv&j!l?fs.
  49. 9 Aug 2005: θ 1π4. 1234. Quantizing Functions. Quantizing Functions. 34. (3.44,0.20). (3.14,0.00). A B. ... n%e5b]h{ag}l.dfe{dBvg(rBn.kms=g}ngBzkmdBa kml:agmb.cjEkp.p e8yfh{ab;kmdfn.an9e{dBjBrBn. ( ). (. ). /20 ,65. 125 0 (. 5 -3 5 ( 12).
  50. 9 Aug 2005: "# "%$& '()%"",-"$."0/1234! "5$ 6879):( ;. 6<$="0+;>$ 6?$@A"$B$ 6?CD%$2! $E. FHGIJG;KMLONQPSRTUTVRKMWPXRKXY[ZFHR]R_XY. badcfe0gihakj akjl monpe0lh q=rphsfe tue0q8vwjh x&yze{g. |} n Ve>a.fedn la.ria0rph } j! #"$%&$%' ($), -./%&,01 , /%' (2 /3,))45
  51. 9 Aug 2005: " $#&%' (),%). -! " #&. / (. 0/ 12 %3&45. 687:9<;>=@?BA.CEDEDECGF HJI=7KCML,;NK;OCG7KCEPOQ(RSFTDEDVUWXF H$Y<;OCM9[ZT=<U<7:ZDGF 7:9]. I =_KP>=H$Y[=;aTb b@cdKefhg-iEe,jkglfhgmfhg-glnoqprfsiGitjVuvlw@xzyOw@{glfEuTj|xk}ifEw@{vlw@xiEe,jmuTjG@xOjOj-wtvzmwy

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