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  1. Results that match 1 of 2 words

  2. How to prepare and deliver a presentation

    mi.eng.cam.ac.uk/~cipolla/archive/Presentations/MakingPresentations.pdf
    23 Apr 2012: 10 20 30 40 50 60. See. Hear. Fear of public speaking. • ... Arial and 32 pt. Arial and 28 pt. Arial and 20 pt.
  3. 19 Jan 2012: There is no space here for the derivation, but [20]gives the details. ... There is no space here for the derivation,but see [20] for details.
  4. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/sxz20_inter11.pdf
    19 Jan 2012: 0 50 100 150 200 2500. 20. 40. 60. 80. 100. ... Just optimising theinference alignment gave2.1% relative reduction. The overallgain from using the SSVM over the VTS-compensated HMMsystem was over 20%, though it should be noted that the SVMand SSVM
  5. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/yw293_ASRU11.pdf
    19 Jan 2012: µ(m)sz = g̃(µ. (m)ye , µ̃l) (20). µ(m)z = J. (m)ye µ. ... Initially, acoustic models were compensatedbased on an initial estimation of the additive noise, using thefirst and last 20 frames of each utterance.
  6. IB-interestpoints.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/old/2012-IB-handout2.pdf
    8 May 2012: Sig. nal. Sigma = 20. As σ increases, the signal is smoothed more andmore, and only the central edge survives. ... This discontinuity canbe detected using correlation. 20 Engineering Part IB: Paper 8 Image Matching.
  7. SSVM_LVCSR_ASRU11.dvi

    mi.eng.cam.ac.uk/~mjfg/sxz20_ASRU11.pdf
    19 Jan 2012: α̃ µ)T. φ(O, w; θ)). (20). One interesting property of expression (18) is that even ifα̃is not well trained, e.g., in the earlier training stage, with aproperµ the ... In practice, for Aurora4 experiments, 1-slack algorithms produce less then
  8. SSVM_LVCSR.dvi

    mi.eng.cam.ac.uk/~mjfg/sxz_TASLP12.pdf
    19 Oct 2012: S TRUCTURE IS CAP TURED BY AM ARKOV NETWORK [20]. Training Unstructured Models Structured Models. ... 20 iterations ofn-slack opti-misation required more around 18G of memory for AURORA4.
  9. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/segdisc_2012.pdf
    19 Oct 2012: Here the term in the exponential becomes. αTφ(O1:T , w1:L) (20). =. ... from a trained system [20]; and an HMM-basedrecogniser on the complete observation sequence [35].
  10. chapter_revise.dvi

    mi.eng.cam.ac.uk/~mjfg/noise_review10.pdf
    10 Feb 2012: This makes them impractical for very rapid adaptation, thoughmodifications to improve robustness are possible [7, 20]. ... 20 M.J.F. Gales. [. µ̂µµ(m)xσ̂σσ (m)2x. ]. =. [. µµµ(m)xσσσ (m)2x. ].
  11. 29 May 2012: Method Optimisation Shape 20 dB 14 dBvts per component diag 8.6 17.4. ... For generativemodels with (“vts’) and without (“vat”) adaptive training this gives an improvementclose to 20 % relative.

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