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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

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: 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.

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

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: 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: 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: 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.

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.

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: 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: citation words, number sequences and prose passageswere used, with one quarter of the data ( 1500 phonemes) setaside as a test set. ... 6. RESULTSResults for both phoneme and word recognition over 50 test utter-ances with.

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.

9 Aug 2005: cd/opal/timit/test/dr4/mbns0/sx320 ). Ampl. itude. h#dhaxn ih r ixs ih nxix gclgaa gclm ey n aa pclbiy w axth ix n w ao kclk ix ng

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

1 Apr 2005: Furthermore, we excludedtdt4 data after 15 Jan-uary 2001 so not to have temporal overlap of acoustic or languagemodel data with the development test sets. ... The resultsstill showed consistent gains over various test sets. Finally we usedthe

9 Aug 2005: The fronto-parallel view reveals how the inter-section tests can be performed efficiently.

28 Jul 2005: As a test,EPZS was applied to synthetic data of the same form as used in Figure 4, where much higherstrains were simulated.

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9 Aug 2005: Text. wfreq2vocab. Vocab. Test text. Perplexity. Id N-gram. LanguageModel. text2idngram. idngram2lm.

9 Aug 2005: high-noise test speechunknown utterance from. high-noise speech. 10 hmms modelling. adjusted set of. ... each state known. cepstral means in. mse. normalised. accumulate. digit-dependentdigit-dependent. noisy test.

9 Aug 2005: XZY[]_acbd[egfihkjmlonqp'jmlsrt]gfZfZeulI[vbw]yxzx{0p|cb+]gfZcj[. Training Data Test Data. |0. |100. |200. |300. |400. |500.

9 Aug 2005: 0.10. ||. ||. ||. ||. |1.00. |. (c) Normalised RMSE -- Approximation Error on Test Data.

9 Aug 2005: Viterbi decoding. Alignment. Output. Test data (text)without <s> and </s>. punctuation stripped with punctuation.

9 Aug 2005: Department of EngineeringUniversity of Cambridge! " $#%&' ( ) " ,%-.) / 0 213. George Francis HarpurQueens’ College. February 1997. A dissertation submitted forthe degree of Doctor of Philosophy. at the University of Cambridge. Summary. The

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