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"Refinements in Hierarchical Phrase-Based Translation…
mi.eng.cam.ac.uk/~wjb31/ppubs/jpino2015HieroRefinementsThesis.pdf6 Feb 2015: Table 1.1: BLEU score obtained by the Cambridge University EngineeringDepartment system and by human translators on the MT08 test set. ... Reference: The US-China Strategic Economic Dialogue mainlydiscusses economic, environmental protection and other -
PROBABILISTIC ACOUSTIC MODELLING FOR PARAMETRIC SPEECH SYNTHESIS Sean …
mi.eng.cam.ac.uk/~wjb31/ppubs/shannon2014probabilistic-thesis.pdf2 Feb 2015: sequences, in the sense of having a very low test set log probability. ... performance but bad test corpus performance. If there is not substantial overfitting then. Results that match 1 of 2 words
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UNICODE-BASED GRAPHEMIC SYSTEMS FOR LIMITED RESOURCE LANGUAGES M.J.F. …
mi.eng.cam.ac.uk/~ar527/gales_icassp2015a.pdf21 Jan 2015: systems. HTK [15] was used for training and test, with MLPs trainedusing QuickNet [16]. ... For all ex-periments in this section, manual segmentation of the test data wasused. -
Association Between Femur Size and a FocalDefect of the ...
mi.eng.cam.ac.uk/reports/svr-ftp/gee_tr696.pdf3 Feb 2015: We fitted the GLM 1 S0 Age. 5i=1 Si Site and then performed an F -test on S0, to test. ... While data exploration is undoubtedly a valuable tool at the researcher’sdisposal, it must be accounted for when making claims of statistical significance, -
Unicode-based Graphemic Systems for Limited Resource Languages
mi.eng.cam.ac.uk/~mjfg/icassp15-gales-babel.pdf18 May 2015: A 2.5 hour test set was also available. For thisLevantine Arabic data no phonetic lexicon was available. ... systems. HTK [15] was used for training and test, with MLPs trainedusing QuickNet [16]. -
main
mi.eng.cam.ac.uk/~cipolla/publications/article/2016-CVIU-EVTTS.pdf10 Sep 2015: 10. ASC group Control group. (N=40) (N=39). MeanS.D. MeanS.D.(Range) (Range) t-test Result. ... Significant main eects for variables involving305more than one level (e.g. in the case of five emotion types), are explored306further using Bonferroni -
lect14
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect14.2up.pdf10 Nov 2015: 1 enrolment utterance/speaker (upto 2 mins). • 3570 test utterances (90% imposters). -
Predicting hip fracture type with cortical bone mapping (CBM)in ...
mi.eng.cam.ac.uk/reports/svr-ftp/treece_tr695.pdf26 Jan 2015: Fit linear model to fracture type, e.g.(age height CM ECTD). Test model on discarded subject and aggregate test results. ... The modelswere assessed for significant difference by comparing the deviances of nested models, using a χ2 test. -
lect13
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect13.2up.pdf10 Nov 2015: each distance measure takes O(d);. • each of n stored point is compared to the test sample,total cost O(nd). -
lect8.dvi
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect8_pres.pdf20 Nov 2015: For a classifier, f(x, θ), trained with parameters θ, the ex-pected test error may be expressed as. ... Consider some standard test data, the Iris data. 1 2 3 4 5 6 7. -
lect13.dvi
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect13_pres.pdf25 Nov 2015: each distance measure takes O(d);. • each of n stored point is compared to the test sample,total cost O(nd). -
Model-based Approaches to Robust SpeechRecognition in Diverse…
mi.eng.cam.ac.uk/~mjfg/thesis_yw293.pdf27 Oct 2015: clean test data. For the CAT system, cluster weights are estimated at the. ... with actual test data. Adaptation techniques have been used to quickly adapt ASR systems. -
lect9_pres
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect9_pres.2up.pdf10 Nov 2015: For a classifier, f(x, θ), trained with parameters θ, the ex-pected test error may be expressed as. ... Thislimits the ability of the classifier. Consider some standard test data, the Iris data. -
lect2.dvi
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect2_pres.pdf10 Nov 2015: r Rat. e (%. ). Future "test" data. Training data. Often, when designing classifiers, it is convenient to have setof held-out training data that can be used to determine theappropriate complexity of -
main
mi.eng.cam.ac.uk/~cipolla/archive/Publications/article/2016-CVIU-EVTTS.pdf10 Sep 2015: 10. ASC group Control group. (N=40) (N=39). MeanS.D. MeanS.D.(Range) (Range) t-test Result. ... Significant main eects for variables involving305more than one level (e.g. in the case of five emotion types), are explored306further using Bonferroni -
Improving Speech Recognition and Keyword Searchfor Low Resource…
mi.eng.cam.ac.uk/~ar527/mendels_is2015a.pdf3 Jun 2015: Before MTWV scoring these valueswere further normalised using a sum-to-one approach to ensurethat the sum over the test set of the scores for each keyword sumto unity. -
Department of Engineering 1 Generative Kernels and Score-Spaces…
mi.eng.cam.ac.uk/~mjfg/Kernel/rcv25_2015_y3.pdf1 Jun 2015: ese scores then form features for the discriminative classiers. is. Test data O. -
Improving Speech Recognition and Keyword Searchfor Low Resource…
mi.eng.cam.ac.uk/~mjfg/interspeech15-mendels-babel.pdf9 Oct 2015: Before MTWV scoring these valueswere further normalised using a sum-to-one approach to ensurethat the sum over the test set of the scores for each keyword sumto unity. -
Annotating large lattices with the exact word error Rogier ...
mi.eng.cam.ac.uk/~mjfg/Kernel/van_dalen-2015-exact_error.pdf16 Jun 2015: 4. ResultsTo test the new algorithm for error-marking lattices, a realistictraining set with long utterances was chosen. -
IMPROVING MULTIPLE-CROWD-SOURCED TRANSCRIPTIONS USING A…
mi.eng.cam.ac.uk/~mjfg/ALTA/publications/van_dalen-2015-improving.pdf11 Feb 2015: It consists ofrecorded proficiency tests for English. The recording quality variesgreatly.
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