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16 Nov 2007: 8.1.1 Training and Test Corpora 102. 8.1.2 System configurations 103. 8.2 Initial Experiments 106. ... ments. A summary of the history of NIST’s benchmark tests are given in [91].

16 Nov 2007: representation 103. 6.5 Synchronous stream system with confidence weighting 107. 6.6 Results using GMM features on WSJ corpus and CSRNAB hub 1 test set 108. ... diagonal transforms 127. 8.4 Experiments using constrained MLLR transforms for WSJ test

15 Aug 2007: Each ofthe 16 conditions also has a test set of a 1001 sentences with 52male and 52 female speakers. ... Exact and Approximate matrix inversion for PCMLLR onAURORA 2 test set A, averaged across N1-N4, WER(%).

23 Apr 2007: In iterative MLLR, a transform isestimated using the 1-Best hypothesis of the test data assupervision. ... However, in some applications, test data becomesavailable gradually. Incremental inference is often used.

20 Jan 2007: All the test sets comprise ofshows where a show is a broadcast of a TV or radio program. ... 3.4. Results. The tests were carried out on a BC test set (dev05bcm: 3 hours,30k words, 5 shows) and two BN test sets (eval04: 1.3 hours, 11kwords, 3

22 Jun 2007: For theBN test set, cross-adaptation in either direction gave gains of about0.5% absolute over the best individual system. ... The first half of this test set was used for tuning the translation.

18 Apr 2007: Sample choosing rough topography pathways. Clearly much better! Try Ar13 as a realistic test. ... How can we extend such sampling methods and tests to much larger systems, e.g.

8 Oct 2007: Hint: Use the Weierstrass M-test from Analysis II to show that these series are locallyuniformly convergent.].

13 Nov 2007: 0.10 Quartz sand 20.3 36.71 34.7 Shear test and tilt table.k Glass plate. ... Test simulations illustrate that the final run-outand propagation time are only slightly affected by this cur-vature.

22 Feb 2007: Require good generalisation– Can reduce training set error rate: need to reduce test-set errors!– Need to keep gains with large numbers of parameters– Need to increase “confusable” data for training– ... simple ASR systems - larger gains–