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  1. Results that match 1 of 2 words

  2. INVESTIGATION OF ACOUSTIC MODELLING TECHNIQUES FOR LVCSR SYSTEMS M.…

    mi.eng.cam.ac.uk/reports/svr-ftp/gales_rt04_modelling.pdf
    19 May 2005: sb(i, j)G(rb) (24). G(rb) =. Mr. m=1. λ(m)bb Gm (25). where sb(i, j) denotes the (i, j)th element of the bth basis ... SATDIAGC cmllr 25.8 17.8 21.9 17.9. SATSPAMcmllr+ 25.0 17.6 21.4 17.6cmllr 24.9 17.5 21.3 17.5.
  3. PRONUNCIATION MODELING BY SHARING GAUSSIAN DENSITIESACROSS PHONETIC…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_euro99.pdf
    9 Aug 2005: One wayto make a fair comparison is to compare the “merged” SLPMsystem with a system that has 24 Gaussians per state. ... However,data sparseness causes the 24 Gaussians-per-state system to beover trained and its WER on the test set is 39.7% which is
  4. Estimating Disparity and Occlusions in Stereo Video Sequences Oliver…

    mi.eng.cam.ac.uk/reports/svr-ftp/williams_cvpr2005.pdf
    9 Aug 2005: For networkswithout loops, message-passing rules can be used to com-pute MAP and MMSE estimates at each node [16, 24]. ... Int. Conf. on Com-puter Vision, volume 2, pages 722–729, 1999. [24] J.
  5. TODO: This is a placeholder. Final title will be filled later.

    mi.eng.cam.ac.uk/reports/svr-ftp/liao_interspeech05.pdf
    26 Sep 2005: Uncertainty 1 4 16 256. an — 33.2. LICENo. 24.6 20.7 17.0 12.3CMLLR 16.3 15.3 12.8 13.5.
  6. Likelihood Models for Template MatchingUsing the PDF Projection…

    mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_bmvc04.pdf
    8 Aug 2005: CS 4.38FL 14.8102RL 185.6105DL 0.08103. CS 4.14FL 8.7102RL 137.0105DL 0.06103. CS 3.49FL 24.94102RL 4.73105DL 5.27103. ... CS 3.07FL 27.88102RL 24.7105DL 1.126103. Figure 4: Hand template matching Rows show some of the images where using the data
  7. A PRACTICAL PERCEPTUAL FREQUENCY AUTOREGRESSIVE HMMENHANCEMENT SYSTEM …

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/logan_icslp98.pdf
    9 Aug 2005: 18 128 23.6 20.1 21.7 27.6 23.2256 18.7 16.5 18.9 23.9 19.5512 18.1 15.5 18.1 24.2

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