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1 - 10 of 26 search results for Economics test |u:mi.eng.cam.ac.uk where 0 match all words and 26 match some words.

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  2. Use of Graphemic Lexicons for Spoken Language Assessment

    mi.eng.cam.ac.uk/UKSpeech2017/posters/k_knill.pdf
    17 Nov 2017: 3. Experimental Results: Automatic Speech Recognition. Data from Business Language Tests (BULATS)I Up to 1 minute spontaneous responses to promptsI ASR training data: 100 hours Gujarati L1 English speechI Test ... Data from Business Language Tests (BULATS

  3. 1 000 001 002 003 004 005 006 007 ...

    mi.eng.cam.ac.uk/~ar527/malinin_acl2017.pdf
    19 Apr 2017: graders for high-stakes tests, maximizing theincrease in performance while rejecting the leastnumber of candidates. ... Lucy Chambers and Kate Ingham. 2011. The BULATSonline speaking test. Research Notes 43:21–25.

  4. A learned emotion space for emotion recognition and emotive speech…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/z_hodari_poster.pdf
    23 Dec 2017: cipan. t rat. ing. Figure 6: Ranksum test. Median rating & 95% confidence interval. ... Stimulation is added to improve interpretability• Evaluation is performed with a perceptual test.

  5. Template.dvi

    mi.eng.cam.ac.uk/~ar527/chen_icassp2017a.pdf
    22 Mar 2017: If thenormalisation termZ(hi) could be approximated as constantC, un-normalised RNNLM probabilities are be used in test time as,. ... In test time, RNNLM probabilities canbe approximated as unnormalised probabilities in Eqn (3) and fastevaluation speed

  6. Phone Classification using a Non-Linear Manifold with Broad Phone…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/l_bai.pdf
    21 Nov 2017: descent using Theano.Evaluation: (i) on all frames in the core test set, (ii) on only centreframes of phone segments (also need to finetune DNN). ... Experimental Results - Phone Classification Performance. (“” indicates a pass of McNamar’s

  7. Automa(c Analysis of Mo(va(onal Interviewing with Diabetes Pa(ents…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/x_wei.pdf
    20 Nov 2017: MI_train: 8h segmented and transcribed data • MI_test: 6 interviews,0.7h in total. • ... Table 1. The ASR results on MI_test. Fmllr feature vectors. Baseline DNN MFCC feature vectors.

  8. Visual gesture variability between talkers in con4nuous visual speech …

    mi.eng.cam.ac.uk/UKSpeech2017/posters/h_bear_poster1.pdf
    23 Dec 2017: Our SI tests use 12 maps derivedusing all speakers confusions bar the test speaker. ... in these tests wetrained and test on the same speaker (maintaining independent samples between train andtest folds).

  9. The University of Birmingham 2017 SLaTE CALL Shared Task Systems

    mi.eng.cam.ac.uk/UKSpeech2017/posters/m_qian.pdf
    20 Nov 2017: A development set, ST-DEV, of 5222recordings and a test set, ST-TST, of 996 recordings were released.System structure:. ... 1 1 0 1 0.  log(score(x) )Step2: Use linear logistic regression to train weights on K systems.Step3: Apply weights on test

  10. 4 BEAR, TAYLOR: VISUAL SPEECH RECOGNITION: A MINI REVIEW ...

    mi.eng.cam.ac.uk/UKSpeech2017/posters/h_bear_poster2.pdf
    23 Dec 2017: sentences 1 to n for speaker N to train, andsentences n 1 to 1,000 for test. ... sentences 1 to. n for speaker X to train, and sentences n1 to 1000 for test.

  11. An avatar-based system for identifying individuals likely to develop…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/b_mirheidari.pdf
    17 Nov 2017: Figure 2: Prototype avatar. Results. A.Speech recognition. Table 1: Speech recognition results.System Train Test WER. ... Train/Test CA AC LX ALL T10A 96.7% 83.3% 66.7% 76.7% 100%B 76.7% 60.0% 50.0% 76.7% 90.0%C 58.3%

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