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19 May 2005: Among all the adaptivelytrained systems, the SATSPAM outperformed all the other sys-tems on both test sets. ... 3. Three BN test sets were used, each of them contains six 30 minutesbroadcast news shows.

26 Mar 2005: SID stage - Adding Eval Data to UBM. • New UBMs, BE and BMAPE, were created using the whole test data set.• System gender labels were used. ... no real cheating but against eval rules).• Using just the target show (rather than whole test set) did

10 Jan 2005: Subsequently, separate distance estimates wereobtained for each patch in the three test sets. ... To facilitate the noise test (3), axial decorrelation curves werealso calculated for each patch.

9 Aug 2005: and kill-tests respectively, are used to decide upon the strengthof the correlation. ... The kill-test succeeds when 8 X , and the following figure illustrates these conditions.

8 Aug 2005: These meth-ods have achieved very good accuracy on a small number ofcontrolled test sets. ... We therefore useDKL(p(0)||p(i)) as a “distance measure” between trainingand test sets.

26 Sep 2005: Here. P (šn|xt, M̌) P (šn|yt, M̌) (10)the model M̌ is now matched to the test condition rather. ... All results are quotedaverage of three of the four available test sets, Feb’89,89 and Feb’91.

9 Aug 2005: At each update a t-test isperformed, determining to a desired level of confidence whetherthe measured mean is lower than would be expected were the can-didates to occur independently. ... The following table gives a brief description ofeach corpus, where

9 Aug 2005: Steps 6, 3, 4 and 5 are then carried out to estimateand test a pronunciation model. ... in Step 5 (Test) PER WER PER WERNone (Dictionary) 49.1% 49.1% 49.5% 58.9%Tree Pron.

9 Aug 2005: The test-set wassubjected to the same preprocessing steps as the training corpus. ... The OOV rate for the describedvocabulary is 0.62% on the test-set.

8 Aug 2005: Test-ing was performed on 164 images containing 193 cars,and164 background images. ... Our technique relies on edge features andhigher-resolution training and test images would certainlyimprove results.

9 Aug 2005: Sincethe integration of feedforward and recurrent MLPs have al-ready been shown to be quite successful, the main goal of thisEsprit project is to develop and test these approaches on largescale ... 2. More specifically our current focus is to compare the

9 Aug 2005: Agiven test example is then classified as belonging to the classwhose boundary maximizes ')( % ,F-. ... all clas-sifiers. 4. TIMIT EXPERIMENTS. To test the performance of SVMs on a more difficult task we usedthe TIMIT database [3].

12 Jan 2005: 10. REFERENCES. [1] NIST, “Benchmark Tests : Rich Transcription (RT),” http://www.nist.gov/speech/tests/rt/. ... ICASSP, March 2005. [19] Linguistic Data Consortium, “Simple Metadata AnnotationSpecification V5,” http://www.nist.gov/speech/tests/rt

10 Jan 2005: nist.gov/speech/tests/rt/rt2003/spring/docs/rt03-spring-eval-plan-v4.pdf , 25th February 2003. ... 3] NIST, “Fall 2004 Rich Transcription (RT-04F) EvaluationPlan,” http://www.nist.gov/speech/tests/rt/rt2004/fall/docs/rt04f-eval-plan-v14.pdf , 30th

9 Aug 2005: We use Isolet1-4 (6240 utterances)to train and speaker-disjoint Isolet5 (1560 utterances) to test. ... The McNemar test finds no significant differences be-tween the Vit or CVit schemes and EM/FL Scheme (at significancelevel. " ).

9 Aug 2005: Figure 3: Annotation Performance on training and test data. The training subset consisted of 1667. ... images, or 90% of the data, with the test subset consisting of the remaining 10%, or 214 images.

9 Aug 2005: g g g g g g. decisionthreshold. Figure 7: Test patterns and decision boundary limits for a ‘g–d’ classifier: a) w too small,b) w too large. ... The recurrent connectionthus updates our estimate of the priors, depending on the previous context, y(t 1

9 Aug 2005: This test data isnamed TestBNAcoustic98. TestBNAcoustic98 comprises 3 hours of acoustic data andthe transcription. ... Table 8 shows the result of the capitalisation generation system based on NE classes andpunctuation marks for these test conditions.

15 Jun 2005: The held-out dataset for these experiments wasthe eval03 test set, which consists of 6 hours of data. ... To examine performance on held-out data the eval03 test setwas used.

9 Aug 2005: In this paper weextend the HME to classification and results are reported for three commonclassification benchmark tests: Exclusive-Or, N-input Parity and Two Spirals. ... The points in the test set are offset vertically from the points in the learning

20 Apr 2005: A validator which uses a classifier to test the regionof the image described by the current state estimate. ... 75 percent thenumber of cycles required by boosting for these. short test sequences (including training the expert).

9 Aug 2005: As the complexity of the tree increases, the test-set perplexity moves through a global minimum. ... Test-set perplexityUnigram at 36.15 and 36.14 for training- and. test-set respectively.

21 Dec 2005: also see [27]). In AFR tests, such methods are usually outperformed by meth-. ... the data as test input. In all tests, both training data for each person in the gallery,.

15 Feb 2005: General System Architecture. The SMD systems used same generic architecture:TEST DATA.

12 May 2005: been introduced, with NIST reporting re-sults with the Wilcoxon signed rank test for speaker-level averagescore differences. ... Woodland, “SU detection forRT-03F at Cambridge University,” http://www.nist.gov/speech/tests/rt/rt2003/fall/presentations/

9 Aug 2005: Facial features are marked by hand and the al-gorithm is run through these test images, making the nec-essary measurements to define each class space. ... d) Proba-bility = 0.9468. 8. Results. We test the algorithm on 100 256x256 images taken

9 Aug 2005: An implementation is outlined and demonstrated on test sequences con-taining two motions. ... The first example considerstwo frames from the “foreman” sequence, commonly used to test motion segmentationschemes.

8 Aug 2005: 0.85. 0.9. 0.95. 1. Test index. Ran. k or. derin. g sc. ... 0.86. 0.88. 0.9. 0.92. 0.94. 0.96. 0.98. 1. Test index. Ran.

14 Sep 2005: These cases are discussed in Section 5. Figure 2: Rotation center placement results for four test scenes (un-blurred in top row.). ... Figure 2 shows results the algorithm’s choice of rotation center for a number of differ-ent motions in four test

9 Aug 2005: Evaluation is performed over various test sets, typically containing 300 sentences from 10–12new speakers. ... The previous bestresult on this test set was 3.6% reported by CMU.

8 Aug 2005: 4. Includ ing EdgeOrientation Impr ovesClassificationPerformance.Thisexampleshowsthe classificationresultson a test setusing a marginalized template.

9 Aug 2005: The global error covariance matrix foreach network mapping is estimated fromits performance on an unseen test set, andthe Jacobian matrix is found by extendingthe usual error back-propagation formulaeto evaluate the

12 Jul 2005: Foreach individual in the database we collected a training and a test video sequenceof the person’s face in random motion, sampled at 10fps. ... Illumination conditions were mildly different in training and test sequences,see Figures 8 and 9.

10 Jul 2005: Training of allalgorithms was performed with data acquired in a single illumination setting and testingwith a single other – we used 9 randomly selected training/test combinations. ... time 7.8 11.8 11.8 0.8 45 7.0 7.0. Table 2:Evaluation results:The

9 Aug 2005: The word error rate resultsare based on the six shows of the 1996 Hub 4 development test,and were generated by rescoring lattices produced by a simplifiedversion of the 1996 Hub

9 Aug 2005: cast News Benchmark Test Evaluation, the general procedures used are described and previous. ... Test Evaluation, four sites (BBN, MITRE, SPRACH and SRI) participated and submitted their.

9 Aug 2005: 62. 6.6 Conclusions. 63. 7 Conclusions 69. 7.1 Acknowledgements. 70. A Significance tests 71. ... A.1 McNemars Test. 71. A.2 Critical ratio for the difference in two s.

9 Aug 2005: A techniquethat optimises the length of individual n-grams is proposed, and experimental tests show it to lead toimproved results.

9 Aug 2005: Training- and test-sets werecreated by splitting the material evenly acro ss these topics in the. ... A 65K vocabulary was used to build language models. Thestandard 2.1 million word set-aside dev-test text for WSJ0 was used as a test-set.

26 Sep 2005: to test it. This was recognised early on, thus most noise robustness methods can be classified under several. ... Test Conditions. Figure 3.1: Methods of reducing the acoustic mismatch. 3.1 Inherently Robust Front-Ends.

9 Aug 2005: A good approximate loglikelihood score was found empirically. To test whether the GS performance degrades below the 1.6 tailthreshold due to the state likelihoods of components within a se-lected

9 Aug 2005: Section 5. evaluates methods using the SQALEUS-English test set and Section 6.

9 Aug 2005: c) aspectratio = 1:1 (201 points). We do a further test by varying the aspect ratio of thepreattentive filter.

8 Aug 2005: Thisis the underlying principle of the Likelihood Ratio Test (LRT). It could be easily shownthat using likelihood ratios minimizes the Bayesian error in two-class decision problems[20].

9 Aug 2005: The calculation of perplexity is based on the probability thatthe language model assigns to some test text. ... Thus perplexity is based solely on the probabilities ofthe words which actually occur in the test text.

9 Aug 2005: hood estimate, then an OpenGL implementation is possible.The model is repeatedly rendered using the 0 -test, with anincreasing probability threshold.

8 Aug 2005: We performed 25 recognition tests, using each database for training and testing it against all the others.For each person in a database we collected a data set consisting of

9 Aug 2005: Test-set perplexity. Training-set perplexity. Test-set, count thresholdprune. 1e-4. 5e-5. 2.5e-5. 1e-5.

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9 Aug 2005: We carried out three additional experiments to test the gen-erality and robustness of our approach:.