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  2. A NEURAL NETWORK BASED, SPEAKER INDEPENDENT, LARGE…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/wernicke_eurospeech93.pdf
    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
  3. ON THE USE OF SUPPORT VECTOR MACHINES FOR PHONETIC ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/clarkson_icassp99.pdf
    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].
  4. paper-mdeval-v19_revised.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/tomalin_rt04.pdf
    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
  5. THE DEVELOPMENT OF THE CAMBRIDGE UNIVERSITY RT-04 DIARISATION SYSTEM…

    mi.eng.cam.ac.uk/reports/full_html/tranter_rt04.html/paper.pdf
    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
  6. LOOSELY COUPLED HMMS FOR ASR H.J. Nock� S.J. Young ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/nock_icslp00.pdf
    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. " ).
  7. johnson05improved.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/johnson_bmvc05.pdf
    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.
  8. THE USE OF RECURRENT NEURAL NETWORKSFOR CLASSIFICATION T. L. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/burrows_nnsp94.pdf
    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. Article Submitted to Computer Speech and Language Automatic…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/kim_csl04.pdf
    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.
  10. 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.
  11. CLASSIFICATION USING HIERARCHICALMIXTURES OF EXPERTS S.R.Waterhouse…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/waterhouse_hme.pdf
    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
  12. TPAMI0110-0304-1 1..13

    mi.eng.cam.ac.uk/reports/svr-ftp/williams_pami2005.pdf
    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).
  13. Variable-length category-basedn-grams for language modelling T.R.…

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/niesler_tr215.pdf
    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.
  14. A Face Recognition System for Access Control using Video ...

    mi.eng.cam.ac.uk/reports/svr-ftp/arandjelovic_PR06.pdf
    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. RT-04 MDE Evaluation Systems at CUED M. Tomalin, S. ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/tomalin_overall_rt04.pdf
    15 Feb 2005: General System Architecture. The SMD systems used same generic architecture:TEST DATA.
  16. STRUCTURAL METADATA RESEARCH IN THE EARS PROGRAM Yang Liu1,5 ...

    mi.eng.cam.ac.uk/reports/svr-ftp/tomalin_icassp05.pdf
    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/
  17. A Probabilistic Framework for Perceptual Grouping of Features for ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/yow_fg96_1.pdf
    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
  18. Edge tracking for motion segmentation anddepth ordering P. Smith, ...

    mi.eng.cam.ac.uk/reports/svr-ftp/auto-pdf/smith_bmvc1999.pdf
    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.
  19. Automatic Face Recognition for Film Character Retrieval in…

    mi.eng.cam.ac.uk/reports/svr-ftp/oa214_CVPR_2005_paper2.pdf
    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.
  20. A single-frame visual gyroscope Georg Klein and Tom…

    mi.eng.cam.ac.uk/reports/svr-ftp/klein_drummond2005BMVC.pdf
    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
  21. 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.

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