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INCREMENTAL BAYESIAN ADAPTATION K. Yu and M.J.F. Gales Engineering ...
mi.eng.cam.ac.uk/~mjfg/yu_ICASSP06.pdf22 Nov 2006: The baseline performance is shown in table 1. The. Incremental Adaptation ML-SI MPE-SI— 32.83 29.20. ... 3. 5 10 15 20 25 30. 29. 30. 31. 32. -
IEEE TRANS. ON SAP, VOL. ?, NO. ??, ????? ...
mi.eng.cam.ac.uk/research/projects/AGILE/publications/mjfg_ASL.pdf23 Feb 2006: This is generallydone in two separate stages, although it is possible to havemore integrated schemes which alter the segmentation duringthe clustering process [19], [20]. ... 0.16 0.18 0.2 0.22 0.24 0.2612. 14. 16. 18. 20. 22. -
Incremental Learning of Temporally-CoherentGaussian Mixture Models…
mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_SME06.pdf14 Mar 2006: Briefly, Zwolinski and Yang [20], and Figueredo and Jain [6] overestimate thecomplexity of the model and reduce it by discarding “improbable” components. ... 20] M. Zwolinski and Z. R. Yang. Mutual information theory for adaptive mixture -
Temporally Varying Model Parameters forLarge Vocabulary Continuous…
mi.eng.cam.ac.uk/~mjfg/sim_INTER05.pdf22 Nov 2006: atj(20). µmjaij. =2hitγ. mlm (t)(otj µmtj ). β̃mlmj(21). σ2mjaij. =2hitatj γ. -
thesis.dvi
mi.eng.cam.ac.uk/reports/svr-ftp/nock_thesis.pdf14 Jun 2006: Speaking Style and Its Effects 20. (F1) than for the more formal, planned studio speech condition (F0) [126]. -
Semi-supervised Learning of Joint DensityModels for Human Pose…
mi.eng.cam.ac.uk/reports/svr-ftp/navaratnam_semi_supervised.pdf14 Sep 2006: ln = 19.99RMS= 20.83. ln = 31.49RMS = 11.61. ln = 71.82RMS= 20.81. ... ln = 98.76RMS= 34.40. ln = 18.46RMS= 10.85. ln = 20.00RMS= 31.02. -
Product of Gaussians for Speech Recognition M.J.F. Gales and ...
mi.eng.cam.ac.uk/~mjfg/airey_CSL06.pdf22 Nov 2006: cmN (ot; µm, Σm) (20). where Km is the normalisation term for the meta-component given in equa-tion 13. ... The form of multiple streamsystem considered here is the synchronous independent stream model imple-mented in HTK(20). -
Hierarchical Part-Based Human Body PoseEstimation R. Navaratnam∗ A.…
mi.eng.cam.ac.uk/reports/svr-ftp/navaratnam_hierarchical.pdf14 Sep 2006: to [20]. The dimensions of this model are generic and not adopted to fit each person. ... 20 highest rankedposes (in 98% of such instances) as illustrated in figure 4. -
Joint Uncertainty Decoding for Noise Robust Speech Recognition H. ...
mi.eng.cam.ac.uk/~mjfg/liao_INTER05.pdf19 Dec 2006: Uncertainty 1 4 16 256. Clean — 33.2. SPLICENo. 24.6 20.7 17.0 12.3FE-CMLLR 16.3 15.3 12.8 13.5. -
C:/SFWDoc/Academic/Publications/2005/ICCV_HCI_2005/FinalPaper/iccv_hci…
mi.eng.cam.ac.uk/reports/svr-ftp/sfwong_iccv_hci05.pdf21 Sep 2006: We used different amount of trainingdata per each pair to re-train the classifier (sample sizes used are 10, 20, 50, 100,300). ... PAMI 20 (1998) 1371–1375. 2. Vogler, C., Metaxas, D.: A framework for recognizing the simultaneous aspects ofamerican
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