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2 Mar 2009: SI — MLLR 24.3 27.0MLLR MLLR 23.6 26.4. DSAT DLT DLT 18.4 28.1MLLRDMT MLLRDMT 22.5 25.3.

20 Aug 2009: There also exists some studies that considerthe above mentioned similarity measures for multimodality fusion of US images with MRI [4, 17,21] and CT [22, 24, 25]. ... M. Treece, and L. Berman. Engineering a freehand 3D ultrasoundsystem. Pattern

7 Oct 2009: 24 language model compo-nents were trained, four components from the STT acoustic data, sixwere newswire texts, and the rest were webdata which was mainlycollected at CUED.

28 Jul 2009: discriminativepower trade-off was explored [24]. It is possible to use each observer as a single compo-nent in a tracking algorithm. ... InFourth Asian Conference onComputer Vision, Taipei, Taiwan, January 2000.1. [24] M.

16 Jul 2009: Cambridge UniversityEngineering Department. JHU Workshop 2009 24. Sequence Kernels for Speaker and Speech Recognition.

4 Aug 2009: is 24.8 mm. One crucial parameter that is missing from Table 1 is the electro-mechanical impulse response. ... 5.4 Width of the transducer elements. For this part of the study, we assume that the FOV and the number of elements are fixed at 24.8.

7 Oct 2009: 24 source-specific components (4 STTacoustic data sets, 6 newswire sources, and 14 webdata sets)arebuilt and merged.

28 Jul 2009: 1.99%. 0.05%. 1.38%. 60.43%. 69.01%. 60.09%. 89.85%. 75.19%. 87.24%. 1.65%. 0.83%. ... The overall score for TextonBoost in training and testing with daytime subsets of our database is 75.02%, and for dusk it was72.24%.

2 Mar 2009: The largest two corpora, giga-cna(0.46) and giga-xin (0.24) were heavily weighted.

28 Jan 2009: 22, 24, 25, 26]. ... Ultrasound in Medicine and Biology, 20(1):27–33,1994. 16. [24] H. Ponnekanti, J.

28 Apr 2009: Cambridge UniversityEngineering Department. Tsinghua University April 2009 24. Model-Based Approaches to Speaker and Environment Adaptation. ... Ratio of corrupted speech magnitudeto clean speech magnitude. • magnitude (α = 1, γ = 1)• power (α = 0,

4 Aug 2009: especially for in vivo applications may be addressed through blind deconvolution [23, 24, 25, 26,.

2 Mar 2009: is introduced to map, for example, ML-trained trans-forms into discriminative transforms [24]. ... This had a gain of 0.24%. k. yu et al.: UNSUPERVISED ADAPTATION WITH DISCRIMINATIVE MAPPING TRANSFORMS 9.

9 Mar 2009: 6 3 ANALYSIS. 20 40 60 80 100sample number. r2=a. r2=24 a.

2 Mar 2009: The primal weight vectorisgiven by. wk =. N. i=1. αiyiβkφk(Oi; λ) (24). ... At each iterationJ(s, β) can be estimated using astandard efficient SVM implementation such as [24].

18 Feb 2009: 24). where γ(m)o,t is the posterior probability of component m given the observation sequence O, bias. ... parameter Mb, and model set M. Differentiating equation 24 with respect to µ̂(rm)b and Σ̂(rm).

28 Jul 2009: discriminativepower trade-off was explored [24]. It is possible to use each observer as a single compo-nent in a tracking algorithm. ... InFourth Asian Conference onComputer Vision, Taipei, Taiwan, January 2000.1. [24] M.

28 Jul 2009: 1.99%. 0.05%. 1.38%. 60.43%. 69.01%. 60.09%. 89.85%. 75.19%. 87.24%. 1.65%. 0.83%. ... The overall score for TextonBoost in training and testing with daytime subsets of our database is 75.02%, and for dusk it was72.24%.

28 Jul 2009: discriminativepower trade-off was explored [24]. It is possible to use each observer as a single compo-nent in a tracking algorithm. ... InFourth Asian Conference onComputer Vision, Taipei, Taiwan, January 2000.1. [24] M.

28 Jul 2009: discriminativepower trade-off was explored [24]. It is possible to use each observer as a single compo-nent in a tracking algorithm. ... InFourth Asian Conference onComputer Vision, Taipei, Taiwan, January 2000.1. [24] M.

28 Jul 2009: discriminativepower trade-off was explored [24]. It is possible to use each observer as a single compo-nent in a tracking algorithm. ... InFourth Asian Conference onComputer Vision, Taipei, Taiwan, January 2000.1. [24] M.