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23 Feb 2006: The finalsystem shows state-of-the-art performance over a range of test sets. ... This approach was not found to perform reliably across differ-ent types of test data.

9 Dec 2006: and probabilitiesFind Ngram rules. human transcriptionand diarisation. (optional)assign categories. test datatraining data. ... Rules whose probability ex-ceeds a threshold are then applied to the test data.

21 Sep 2006: cluttered background, and background with skin colour). The overallaccuracyon 1025 test cases is 89.7%. ... Thepercentage of test cases that cannot be mapped into any classis 20.3%.

22 Nov 2006: The various models andrecognition configurations are evaluated using several recent BNdevelopment and evaluation test sets. ... Test set # Shows Hours Period. dev03 6 3 Jan. 2001eval03 6 3 Feb.

22 Nov 2006: Only 0.3% improvement wasobtained on this test set. 3See http://htk.eng.cam.ac.uk/docs/cuhtk.shtml4Significance tests were carried out using the NIST Scoring Toolkit. ... These systems wereevaluated on thedev03 andeval03 test sets, each consistingof 3

5 Jul 2006: Evaluation on held-out data (eval03)– 6 hours of test data– decoded using LVCSR trigram language model– baseline using confusion network decoding. ... φcn(O; λ) =[ F(ω1) F(ω2). φ(O; λ). ]. • Incorporating in score-space requires consistency

29 Apr 2006: To establish baseline performance, we compared ourrecognition algorithm to:. • State-of-the-art commercial system FaceItr by Identix[8] (the best performing software in the recent FaceRecognition Vendor Test [10]),. • ... perform well if imaging

15 Feb 2006: e) A test input with textons t1 and t2 in the same relative positionas that of training. ... Quantitative evaluation. Figure 8 shows the confusion matrix obtained byapplying our algorithm to the test image set.

22 Nov 2006: 3. INCREMENTAL BAYESIAN ADAPTATION. The Bayesian adaptation discussed in section 2 runs in a batchmode where all test data are assumed to be available before adap-tation. ... Theperformance was evaluated on the 2003 evaluation test dataset,eval03,

22 Nov 2006: Evaluation on held-out data (eval03)– 6 hours of test data– decoded using LVCSR trigram language model– baseline using confusion network decoding. ... φcn(O; λ) =[ F(ω1) F(ω2). φ(O; λ). ]. • Incorporating in score-space requires consistency

21 Sep 2006: The parameters for both methods are setby testing the classification performance on a test setof 5000 images. ... In a first approach,the edge and colour cost terms are computed for a numberof test images.

3 Apr 2006: In [14], error is computed using a“leave-one-out” test rather than with completely new testdata. ... A leave-one-out test for gaze-tracking data with theS3GP gives an error of 0.68.

22 Nov 2006: This is used both within a cross-validationframework and with a held-out test set, the eval03 dataset [20]. ... This is easilydemonstrated on the 6-hour eval03 test set [20]. SVMswere trained to disambiguate ten of the most confusablepairs.

17 Feb 2006: In AFR tests, such methods are usuallyoutperformed by methods from the third class: view-based techniques e.g. ... KLD) [38]. In all tests, both training data for each person in the gallery, as well as test data,consisted of only a single sequence.

22 Nov 2006: Systems aretrained using the 296 hours switchboard data (h5etrain03 )and evaluated on a 3-hour test set (dev01sub ). All systemsin this paper used 12 PLP coefficients with theC0 term plusthe first, ... 1Significance tests were carried out using the NIST

22 Nov 2006: Thus, the final fea-ture vector has 42 dimensions. Results are given on two test sets:dev04f consists of 0.5 hours of CCTV data from shows broad-cast in November ... Thiseffect is seen for both complementary models, on both test sets.

19 Mar 2006: This is challenging across large posevariations, such as those contained in our test set. ... Face recognition vendor test 2002. Technical report,National Institute of Standards and Technology, 2003.

22 Nov 2006: Table 1: Clean, matched andSPLICE on AURORA 2.0 test set A,averaged across N1-N4, WER(%). ... M-Joint1 2.43 3.82 6.97 17.1416 1.95 2.80 4.23 9.89. Table 2: Model-basedJoint systems’ performance on AURORA2.0 test set A, averaged

18 Sep 2006: create a set of such images for their tests in [7]), avideo taken of the object in its environment (e.g. ... The first setting was used as training, with the others used as test sets.

19 Dec 2006: When models trainedin clean conditions are used in the real world, the mismatchbetween the training conditions and the test causes significantloss in recognition accuracy. ... Here. P (šn|xt, M̌) P (šn|yt, M̌) (10). where the model M̌ is now