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11 Jan 2008: The selected data are addedto the original training dataset to update the acoustic model and op-tionally the language model [3, 1, 4].

25 May 2004: To evaluate MPE-based DLT for supervised adaptation, thefull decoding with 5K word vocabulary and a bigram LM is op-erated and then the generated lattices are expanded with a trigramLM

20 Feb 2018: Hence defining an op-timal summary policy is not so obvious. If f is chosenwell, however, then one could hope that the optimal ac-tion is dependent only on f (b).

11 May 2010: The way to achieve this robustness is to utilize in-terest points in computing the descriptor as op-posed to the raw image data.

10 Nov 2014: More specif-ically, for the IV search, each query is converted to a wordweighted finite state acceptor (WFSA) and a composition op-eration is carried out with the word index in

13 Mar 2018: This goal is achieved by solving an op-timization problem by numerical techniques, searching di-rectly for the intrinsic parameters of the cameras, instead ofthe indirect search performed by the algorithms

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15 Jun 2005: This comes directly fromequation 19. It is interesting to contrast this to MMI training [11].In MMI training the average posterior of the correct label is op-timised.

9 Aug 2005: M<M<M<MN<N<N<N<NN<N<N<N<N. Phoneme. O?OO?OO?OP?PP?PP?P Q<Q<Q<Q<Q<QR<R<R<R<R

13 Mar 2018: rize human action and gesture classes in videos. Traditional. approaches based on explicit motion estimation require op-.

13 Mar 2018: LeCun. Sceneparsing with multiscale feature learning, purity trees, and op-timal covers.

13 Mar 2018: Hand Tracking Using A Quadric Surface Model. R. Cipolla1 B. Stenger1 A. Thayananthan1 P. H. S. Torr2. 1 University of Cambridge, Department of Engineering, Trumpington Street,Cambridge, CB2 1PZ, UK. 2 Microsoft Research Ltd., 7 J J Thomson Ave,

13 Mar 2018: ntr. op. y L. os. s. Inp. ut. Ima. ge. L1.

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13 Mar 2018: The reconstructed surface is obtained by solving the op-timisation. Smin = arg minSC. ... Ourmethod has no concept of a current surface during the op-timisation phase, and therefore has to make the visibilityassumption of section 3.

16 Nov 2007: Discriminative Complexity Control. and Linear Projections for Large. Vocabulary Speech Recognition. Xunying Liu. Clare Hall. University of Cambridge. September 2005. Dissertation submitted to the University of Cambridge. for the degree of Doctor of

13 Mar 2023: 3. We apply the attention-based relevancy maps tied to vision transformers to guide op-timal transport optimization and further validate the effectiveness of partial optimaltransport for dataset showing strong viewpoint

12 Aug 2019: Recently, Park etal. [35] proposed a refinement method by computing an op-tical displacement map in the same 2D planar domain of thephotometric stereo images.

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13 Mar 2018: Figure 11 illustrates the op-eration of the classifiers at different levels of the tree.

13 Mar 2018: If the angles of the epipolar lines had op-posite signals in respect the horizontal axis, the points putunder correspondence by the epipolar geometry would be atopposite places in the bottom

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13 Mar 2018: Having maximised the likelihood of each primitive, amodel selection criterion is used to decide whether the op-tional parameters A?

5 Jun 2006: There are many“noise” sources that may affect the speech signal. Firstly, there is the problem of the acoustic environment in which the system is op-erating.

13 Mar 2018: Int J Comput VisDOI 10.1007/s11263-010-0381-3. Incremental Linear Discriminant Analysis Using SufficientSpanning Sets and Its Applications. Tae-Kyun Kim Björn Stenger Josef Kittler Roberto Cipolla. Received: 14 December 2009 / Accepted: 10

13 Mar 2018: However, in contrast to image classification and somedeep learning lead problems of computer vision, semanticsegmentation (especially for autonomous driving) still op-erates on limited size datasets which do not exceed 5000labelled

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26 Jul 2016: Structured and InûniteDiscriminative Models for. Speech Recognition. Jingzhou YangHomerton College. Department of Engineering. University of Cambridge. A thesis submitted to the University of Cambridge for the degree of. Doctor of Philosophy.

13 May 2004: Cluster Voting for Speaker Diarisation. S.E. TranterCUED/F-INFENG/TR-476. 1st May 2004. Abstract:. It is often important to be able to automatically detect ‘who spoke when’ in audio data. The speaker di-arisation task attempts to address this

7 Nov 2013: designed to optimise KWS performance. They are therefore not op-. timal in terms of raw STT performance, for example currently the.

20 Feb 2018: However,approx-imate solutions can still provide useful policies. The simplest ap-proach is to discretise belief space and then use standard MDP op-timisation methods [6].

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13 Mar 2018: This goal is achieved by solving an op-timization problem by numerical techniques, searching di-rectly for the intrinsic parameters of the cameras, instead ofthe indirect search performed by the algorithms

13 Mar 2018: Hand Tracking Using A Quadric Surface Model. R. Cipolla1 B. Stenger1 A. Thayananthan1 P. H. S. Torr2. 1 University of Cambridge, Department of Engineering, Trumpington Street,Cambridge, CB2 1PZ, UK. 2 Microsoft Research Ltd., 7 J J Thomson Ave,

29 May 2004: Structural op-timization was then performed in two stages. First the number ofcomponents per state was determined for a standard non-HLDA 28component system.

20 Feb 2018: S. sn. nsbsb. 1. )()(maxargˆ)( υππ (16). Thus the value-function method provides both a parti-tioning of belief space into regions corresponding to op-timal actions as well as the

16 Nov 2007: Op-Room noise for noise matched models retrained with corrupted training data 119. ... ment a clean speech system on RM task corrupted with Op-Room noise at 18dB.

20 Feb 2018: Thisprovides increased robustness to errors in speechunderstanding and automatic dialogue policy op-timisation via reinforcement learning (Roy et al.,2000; Zhang et al., 2001; Williams and Young,2007; Young et al.,

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20 Feb 2018: This Gaussian process op-erates on a continuous space dialogue rep-resentation generated in an unsupervisedfashion using a recurrent neural networkencoder-decoder.

2 Nov 2000: recognition strategy. The best overall results were obtainedusing 1003 classes and a 50 topic model with weights op-timised using the EM algorithm.

28 Jun 2011: For the in vitro scan, we used a ULA-OP (Università degli Studi di Firenze, Italy) scannerwith a LA523 (Esaote, Genoa, Italy) linear array probe.

13 Jun 2013: The second application of sum-to-one normalization op-timizes the term-weighted value for queries with different frequen-cies of occurrence.

31 Mar 2005: The reconstructed surface is obtained by solving the op-timisation. Smin = arg minSC. ... Ourmethod has no concept of a current surface during the op-timisation phase, and therefore has to make the visibilityassumption of section 3.

11 Jan 2008: Rather than directly op-timize the interpolation weights, their prior distribution and the as-sociated hyper-parameters are optimized.

8 Mar 2000: 3.4. Variability of decode speed. Another concern when designing a system for guaranteed op-eration in under 10 times real time is the variability in decod-ing speed over different

20 Feb 2018: NFp;. 2where Q is the set of all phone models and NF(p)the number of frames in the acoustic segment Op. ... Hence, the denominator score is de-termined by simply summing the log likelihood perframe over the duration of Op.