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1 - 10 of 25 search results for katalk:PC53 24 / |u:mi.eng.cam.ac.uk where 0 match all words and 25 match some words.

  1. Results that match 1 of 2 words

  2. Towards Learning Orientated Assessment for Non-native Learner Spoken…

    mi.eng.cam.ac.uk/~kmk/presentations/ALTA_Sheffield_20190306.pdf
    8 Mar 2019: 300 300. 25.5. 400 24.5. 400 24.4. ASR on Non-native Speech (2). • ... Thai dh d 7.24 oh aa 5.21. 30. • Top 2 recurrent substitution errors for speakers in each L1.

  3. LEARNING BETWEEN DIFFERENT TEACHER AND STUDENT MODELS IN ASR ...

    mi.eng.cam.ac.uk/~mjfg/ALTA/ASRU2019_TS.pdf
    20 Dec 2019: The derivatives of the per-frame. observation log-likelihoods with respects to the parameters are [24]. ... Work in [24] suggests several methods to improve gra-dient descent training of a GMM.

  4. BI-DIRECTIONAL LATTICE RECURRENT NEURAL NETWORKSFOR CONFIDENCE…

    mi.eng.cam.ac.uk/~ar527/ragni_icassp2019.pdf
    5 Feb 2019: may include embeddings [24], acoustic andlanguage model scores and other information. ... 24] T. Mikolov, I. Sutskever, K. Chen, S. S. Corrado, and J.

  5. Applying Deep Learning in Non-native Spoken English Assessment

    mi.eng.cam.ac.uk/~kmk/presentations/APSIPA2019_Knill_Keynote.pdf
    21 Nov 2019: 1.0 indicates within one CEFR grade-level. 24/45. Assessment System Performance. • ... 1.0 indicates within one CEFR grade-level. 24/45. Performance Analysis. 25/45.

  6. CONFIDENCE ESTIMATION AND DELETION PREDICTION USINGBIDIRECTIONAL…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/SLT2018_ragni.pdf
    31 Aug 2019: Thesefeatures may include various statistics extracted from audio, acousticmodels, language models and lattices [24]. ... 24] T. Schaaf and T. Kemp, “Confidence measures for spontaneousspeech recognition,” in ICASSP, 1997.

  7. BI-DIRECTIONAL LATTICE RECURRENT NEURAL NETWORKSFOR CONFIDENCE…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ICASSP2019_li.pdf
    31 Aug 2019: may include embeddings [24], acoustic andlanguage model scores and other information. ... 24] T. Mikolov, I. Sutskever, K. Chen, S. S. Corrado, and J.

  8. SEQUENCE TEACHER-STUDENT TRAINING OF ACOUSTIC MODELS FOR…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/wang_slt18.pdf
    25 Feb 2019: It calculates the denominator by directly applying forward-backward computations [23, 24] on an unpruned denominator graphon GPU hardware. ... In Proc. ICASSP, volume 2,pages 605–608, 1996. [24] P. C. Woodland and D.

  9. POUDEL, LIWICKI, CIPOLLA: FAST-SCNN: FAST SEGMENTATION NETWORK 1…

    mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2019-BMVC-Fast-SCNN.pdf
    12 Aug 2019: 1.1 ContributionsCurrently, semantic segmentation is typically addressed by a DCNN [2, 18, 24, 30]. ... arXiv:1801.04381 [cs], 2018. [24] E. Shelhamer, J. Long, and T. Darrell.

  10. IMPROVED AUTO-MARKING CONFIDENCE FOR SPOKEN LANGUAGE ASSESSMENT M.…

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/vecchio_slt18.pdf
    25 Feb 2019: AUCr =AUCmodel AUCradom. AUCoptimal AUCradom. (24). where AUCradom, AUCoptimal and AUCmodel represent thearea under the random, optimal and model back-off curvesrespectively.

  11. To appear Proc. ICASSP. c©2019 IEEE. Personal use of ...

    mi.eng.cam.ac.uk/~mjfg/ALTA/publications/Knill_ICASSP2019_AcceptedPaper.pdf
    3 Mar 2019: 24.3 23.6 21.0 25.2.

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