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8 Mar 2000: Figure 3: Network prediction on training and test data. Actual output[.

6 Oct 2000: AHA Architecture and Historyof Art. Biol BiologyCl ClassicsDiv DivinityEduc EducationEngg EngineeringEngl EnglishEP Economics and Politics. ... Economic History. Prof. P. Thane, , Prof. P. S. Dasgupta, JN, .Prof.

8 Feb 2000: validated against experimental data from a ten-sion test and a bending test on flat plates. ... b)(a). Figure 6: Tension and bending tests on composite plates. The tension test, Fig.

20 Mar 2000: Useful design guidelines about possible materials and shapes. Useful guidelines about the economics to enable selection of cost-effective processes.

28 Jan 2000: Economics. We've already ruled out machining on cost grounds. Can any of the other processes be ruled out because of cost? ... We're going to want to make millions of pins, so we need a process with an appropriate economic batch size.

15 Mar 2000: AHA Architecture and Historyof Art. Biol BiologyCl ClassicsDiv DivinityEduc EducationEngg EngineeringEngl EnglishEP Economics and Politics. ... Economic History. Prof. P. Thane, , Prof. P. S. Dasgupta, JN, .Prof.

25 Oct 2000: 5.4 Results. The first experiment,to test the algorithm, usestwo imagesof a calibrationgrid.

26 Apr 2000: distance. This makes the complete test for distance initialisation as follows:. • If the voxel is not initialised, store the new distance and angle. •

27 Sep 2000: VTLN warping in training and test both for GI and GDmodels for full and reduced bandwidth coding. ... 8. ACKNOWLEDGEMENTS. We would like to thank BBN for providing the MiniTrain trainingand MTtest test set definitions.

10 Apr 2000: Reducing Word Error Rates of Found Speech -XPERT Tool for Transcription Analysis. S.E. JohnsonCUED/F-INFENG/TR 330. July 1998. Speech, Vision and Robotics GroupCambridge University Engineering Department. Trumpington StreetCambridge CB2 1PZ.

31 Jul 2000: Pronunciation modeling by sharing Gaussians 141. TABLE I. WER degradation with speaking style on theMULTI-REG test set. ... Steps (6), (3), (4) and (5) arethen carried out in that order to estimate and test a matched pronunciation model.

2 Mar 2000: IfX F8,5, then P(X 4.82) = 0.95. The tables can be used to conduct various hypothesis tests with sizes 0.01, 0.05and 0.10. ... For the F-distribution, only the 95% point is shown; this is what is neededto conduct a one-sided test of size 0.05.

10 May 2000: The TREC-8 SDR test collection[1] usedfor the experiments consists of 21,754 spoken documentsand 49 written queries. ... expansion. Thisparallel collection was larger and thought to be more re-liable than the automatically transcribed test collection.

4 Dec 2000: As a by-product this generates a test of SCT geometry within AGDD/XML and G4Builder framework.

5 Oct 2000: While MMIE is very effective at reducing training set error a keyissue is generalisation to test data. ... On other test setsimprovements greater than 1% absolute have also been obtained and.

17 Oct 2000: standard clean modelset and then to perform further adaptation for the actual test data [7]. ... For the evaluation test H3-P0 data one microphone was selectedby NIST for each speaker.

8 Mar 2000: approach, and two sets of HMMs were adapted.The test data for F2 was also automatically labelled as narrow. ... 1995 Hub-4 “Dry-Run” Broadcast Materials Bench-mark Tests. Proc. DARPA Speech Recognition Workshop, Har-riman, New York.

31 Jul 2000: nemea. Training data Average log2-prob (eciency). ICSI-test TIMIT-test. TIMIT 0.76! 0.60. ... derivation of the training transcrip-tions, and the hand-labelling of the test set.

5 Oct 2000: 4 IMPROVING MMIE GENERALISATION. An important issue in MMIE training is the ability to generalise tounseen test data. ... The results of these experiments on both theeval97sub and eval98 test sets are shown in Table 4.

6 Nov 2000: ing in general) is the generalisation performance i.e. the dif-ference between training set and test set accuracy. ... Baseline Models and Hub5 Training/Test DataThree different training sets and three different test sets.