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17 Oct 2000: 704/c 26.3 15.570a/b 10.3 1.970f/d 18.8 7.670w/c 24.2 15.8. Table 1: Lattice error rates for several development test speakerswith ... a fg 14.24 —HMM-2 thresh. b fg 13.81 —HMM-2 thresh. c fg 13.71 6.68.

20 Dec 1999: Triangulation Each sample point is surrounded by 24 tetrahedra, and each tetrahedronis associated with 4 sample points. ... Computer GraphicsForum, 15(4):191–203, 1996. [24] G. M. Treece, R. W. Prager, A.

16 Jan 2008: 2.2.2 Hidden conditional random fields (HCRFs). 24. 2.2.3 Parameter estimation for conditional models.

26 Sep 2016: IEEE, 2011, pp.24–29. [9] R. Gemello, F. Mana, S. Scanzio, P.

8 Jul 2003: Disfluency Results. System Edit (BN=84,CTS=521) Filler(BN=143,CTS=840) Total( Context? ) Miss FA Error Miss FA Error ErrorBN-ASR () 91.67 28.57 120.24 48.95 ... 87.41 136.36 130.40BN-ASR (X) 91.67 15.48 107.14 48.95 82.52 131.47 122.47BN-REF () 83.33

31 Aug 2019: may include embeddings [24], acoustic andlanguage model scores and other information. ... 24] T. Mikolov, I. Sutskever, K. Chen, S. S. Corrado, and J.

9 Oct 2015: Telugu. VLLP 0.838 57.0Blogs 0.128 2625.6News 0.000 893.0Subtitles 0.024 24.9TED 0.000 18.8. ... 24] Z. Tüske, J. Pinto, D. Willett, and R. Schlüter, “Investigation oncross- and multilingual MLP features under matched and mis-matched acoustical

8 Mar 2000: M S T. M 82.11 17.89 0.00N 15.27 84.22 0.51S 0.56 98.24 1.20T 0.00 1.19 98.81.

30 Mar 2006: Examples includequasistatic compression imaging [26, 29], axial shear wave imaging [32] and acoustic radiationforce imaging in both quasistatic/impulsive [24] and dynamic [2] forms. ... We substitute this into Equation 24, and rearrange to produce a

12 Sep 2018: 23] L. Breiman. Bagging predictors. Machine learning,24(2):123–140, 1996. [24] O. Siohan, B. ... IEEE/ACM Transactions on Audio, Speech,and Language Processing, 24(8):1438–1449, 2016. Introduction. Graphemic English systems.

14 Sep 2010: yt µ. (ωjM)y. ). . (24). where the clean models have been compensated for the test condition Y,λ = {λ(ωl)y , λ(ωj)y }, and there are a total of ... 05 8.78 8.43 6.20 9.53 8.22. 00

22 Mar 2017: These weretrained on FLP data of 24 Babel languages and CTS data of 4 addi-tional languages, English, Spanish, Arabic and Mandarin, releasedby LDC. ... Stacked Hybrids were trained withand without stimulated training using monophone initialisation

21 Sep 2006: 21] for upper bodypose estimation. In [24] it is suggested to partition the parameter spaceof a 3D hand model using a multi-resolution grid. ... range. At detection rates of 0.99 the false positiverate for the centre template is 0.24, wheras it is

9 Oct 2015: Acoustic likelihood combination and lat-tice combination have been compared in [24]. ... Speech and Audio Processing, vol. 2, no. 1,pp. 217–223, 1994. [24] P.

8 Mar 2000: This system gave a word error rate of 24.1% on. the TREC-6 test data. ... 1 77.3 17.5 5.2 5.9 28.6HTK-2 80.8 14.6 4.6 4.9 24.1.

3 Sep 2010: This chap-ter examines approaches that handle background-noise and channel distortions bymodifying the parameters of the underlying acoustic models, in this case HiddenMarkov Models (HMMs) [52, 24]. ... Though intuitively well motivated, from (24) it can

5 Sep 2008: µPRA = pW TWS TSI TspI′. (24). This is a measurement of system accuracy, and includes errors from every component of thesystem.

29 Apr 2011: Any systematic measurementbias that affects the baseline and 24 month density estimates to different degrees will manifest themselvesas false thickness effects. ... In this instance, thereis a known source of bias: the 24 month scans extended, on average,

16 Nov 2007: 2.3 Limitations of HMMs for Speech Recognition 24. 2.3.1 Explicit Temporal Correlation Modelling 25.

10 Dec 2003: Results of pairwise system combination using CNC:. System A B C D23.0 23.6 23.4 24.8.

4 Sep 2004: top right 0.17 0.28 0.07 0.24 < 0.30bottom left 0.22 0.19 0.27 0.35 < 0.39bottom right 0.28 0.27 0.28 ... 3.5 MHz centre 0.24 0.43 0.33 0.14 < 0.43(12 cm) top left 0.43 0.55 0.55 0.49 < 0.75.

4 Aug 2014: 0.3 t < 1.0 1.06 0.37 2.35 1.46 0.58 1.07 0.24 0.14 0.15 0.23 0.04 0.31. ... 0.3 t < 1.0 2 31 24 60 2 35 3 30 3 30 3 30.

15 Feb 2006: For cases like these, the algorithm of [24] couldbe used to refine the class labeling. ... In: AAAI.(2005) 1508–1513. 24. Kumar, S., Hebert, M.: A hierarchical field framework for unified context-basedclassification.

5 Oct 2000: Lattices were generated on the training set using a bigramLM. The bigram 1-best hypotheses had a 24.6% word error rate(WER) and a Lattice WER (LWER) of 6.2%.

12 May 2016: CUED Lorelei Team. Babel ProgramSeminar at Cambridge University April 2014 24.

7 Oct 2023: outliers in the output of the corner detector. 24 Engineering Part IIB: 4F12 Feature extraction.

12 May 2016: CUED Lorelei TeamBABEL Program. Seminar at Edinburgh University February 2014 24.

22 May 2006: Communications of theACM, 24(6):381–395, 1981. 15. [5] A. H. Gee, R. ... H. Berman. Engineering a freehand 3Dultrasound system. Pattern Recognition Letters, 24:757–777, 2003.

23 Jun 2005: 15. 16. 17. 18. SID CLR Threshold. Dev. 24 D. ER. ... Seg. men. t Im. pu. rity. on. dev. 04 (. 24 s.

19 Jan 2023: 24. GEC. 25. seq2seq GEC. Treat GEC as a machine translation problem i.e.

22 Jul 2003: CU-DIARY 0.2 9.1 24.0 33.29 2.1 1.5 5.9 9.5MIT-DIARY 2.7 5.6 16.1 24.46 2.8 1.7 ... 1 24.23 2.6 24.7 2.1 43.60PERF 0.2 9.1 0.4 11.60 0.2 29.7 0.4 32.20.

30 Mar 2007: In theplane of the images, the offset is found using standard image registration and motion trackingtechniques [2, 24].

23 Apr 2024: More recently, Leong et al. [24] use a variational autoencoder(VAE) [20] to learn latent DXA encoding, and map 3DO scans to pseudo-DXA images. ... Mean bias was 0.24 (6.7; 6.2)% for percentage body fatand for the other body composition metrics, the mean

10 Aug 1999: F =. 24 0 0 c0 0 da b e. 35 : (4). ... F =. 24 0 0. 0 sin0. 0 0 0 cos0sin cos p1 2 02.

25 May 2004: 16 20 24 28. dev03 bnac 144 17.9bnacTDT4 375 16.8bnacTDT4TDT2 749 16.8 16.5 16.4bnacTDT4TDT2TDT4a 1126 16.7 16.3 16.0 16.1bnacTDT4TDT4a 752 16.7

21 Sep 2006: Pattern Recognition Letters, 24(2003):2743–2749, 2003. 8. O. Yamaguchi, K. Fukui, and K.

13 Mar 2018: recognition from face motion manifolds.Image and Vision Computing, 24(5),. 2006. (in press). ... 24] R. O. Duda, P. E. Hart, and D. G. Stork.Pattern Classification.

12 Jan 2010: 21 0.78464 1.10 1.05 0.47 0.75 0.10 0.54 0.05 0.55465 0.95 1.18 0.24 0.96 0.02 0.68 ... 70 0.22 0.54 0.19 0.54all 1.24 0.83 0.19 0.62 0.01 0.48 0.01 0.47.

25 May 2007: imaging was first tested clinically for breast scanning [4] and breast screeninghas ever since been a key driver for research [7, 23, 24]. ... Berman. Engineering a freehand 3D ultrasound system.Pattern Recognition Letters, 24(4–5):757–777, February

29 Sep 2006: 1.24. 1.26. 1.28. 1.3. 1.32. 1.34x 10. 3. scatterer depth (m). ... f) Window length (max=175), strain and SNRe forDRS, SNRe=12.87 and V=5.24.

28 Jul 2005: Alam et al. [24] among others have noted that speckle tracking inthis form is suboptimal for elastography, because tissue compression causes both displacements(which are estimated) as well as within-window

19 May 2014: 1) (2) (3) (4). (9)(8)(7)(6). (5). (24). Learned contour and texture.

19 Mar 2008: Foundations and Trends R inSignal ProcessingVol. 1, No. 3 (2007) 195–304c 2008 M. Gales and S. YoungDOI: 10.1561/2000000004. The Application of Hidden Markov Modelsin Speech Recognition. Mark Gales1 and Steve Young2. 1 Cambridge University

11 Feb 2004: Tsai [24] introduces a rich camera model includingradial distortion and proposes a two-stage calibration tech-nique, consisting of an initial linear solution followed by anonlinear optimisation. ... Visual servoing-basedaugmented reality. In Proc. IEEE

16 Nov 2007: 2.5 HLDA and LDA projection 24. 2.6 multiple HLDA projections 25.

27 Oct 2015: 21. 2.3.3.2 Discriminative Training. 24. 2.4 Recognition of Speech Using HMMs. ... Normally,. a truncated DCT transform is used, i.e., Lc( 13) is smaller than Lf ( 24) and the high ordercepstral coefficients are discarded.

28 Jul 2004: 4] M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysisand automated cartography.Graphics and Image Processing, 24(6):381–395, 1981.

11 Nov 2005: 1994). Dynamic be-lief networks for discrete monitoring. IEEETransactions on Systems, Man, and Cybernet-ics, 24(11):1593{1610.

5 Dec 2003: fewer parameters than a setof 24 3D points in general position.

25 Oct 2000: Figure 2: Resultsfrom the existing SpaceCarving algorithm using different thresholdsettings. The voxel array size was , and the thresholdswere 48,32,24,16(of 255)respectively. ... Figure 4: Resultsfrom the existing SpaceCarving algorithm using different