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51 - 60 of 1,000 search results for katalk:PC53 24 / |u:mi.eng.cam.ac.uk where 0 match all words and 1,000 match some words.

  1. Results that match 1 of 2 words

  2. EFFECTS OF THE USER MODEL ON SIMULATION-BASEDLEARNING OF DIALOGUE ...

    mi.eng.cam.ac.uk/~sjy/papers/swsy05.pdf
    20 Feb 2018: The COMMUNICATOR systems in contrast onlyrequest between 24% and 43% of the unknown slots in each state.

  3. BN-E Experiments in Cambridge Do Yeong Kim, Mark Gales, ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/kim_sttmar05.pdf
    12 Apr 2005: 302k 9k+ 16.0 13.9 24.8 –MLE 415k 9k+ 16.0 13.5 24.3 –. 398k 12k+ 16.1 13.6 24.5 –. 302k 9k+ 13.2 11.2 ... dev04 eval03 dev04f. MLEMPron 16.0 13.6 24.5SPron 15.6 13.5 24.2. MPEMPron 12.9 11.1 19.1SPron 12.7 10.8 18.8.

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

    mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/APSIPA2019_Knill.pdf
    21 Feb 2022: 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.

  5. Structured Discriminative Models Using Deep Neural-Network Features

    mi.eng.cam.ac.uk/~mjfg/asru15-vanDalen.pdf
    11 Mar 2016: MPE— 7.15 11.06 14.37 24.54 16.79CML 6.95 11.00 14.29 24.39 16.68large-margin 7.02 10.92 14.16 24.28 ... Therefore the systems use graphemic lex-ica generated using an approach which is applicable to all Unicodecharacters [24].

  6. CAMBRIDGE UNIVERSITY ENGINEERING DEPARTMENT DISCRIMINATIVE…

    mi.eng.cam.ac.uk/~mjfg/gales_tr605.pdf
    13 Aug 2008: In common with other work in this. 5. area [9, 24], G is approximated by the diagonalised empirical covariance matrix of the trainingdata. ... 00 25.48 21.73 25.95 24.46 21.64 26.05 24.51 22.56.

  7. AUTOMATIC COMPLEXITY CONTROL FOR HLDA SYSTEMS X. Liu, M. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/liu_icassp2003.pdf
    19 Sep 2003: Fig. 1. Test set word error rate for all possible models, with thestandard front-end 12, 16 and 24 component performance. ... The best perfor-mance, 36.8%, was obtained using 24 components per state and anHLDA projection from 52 dimensions to 38

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

    mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/ALTA_Sheffield_20190306.pdf
    21 Feb 2022: 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.

  9. eps.dis.dur.testa.eps

    mi.eng.cam.ac.uk/~mjfg/gales_ASRU09.pdf
    14 Sep 2010: Using 17 pairs, about 24% of thetotal number of pairs, 92% of the WER improvement usingthe 1-v-1 system over the VTS baseline was achieved.

  10. Statistical User Simulation with a Hidden Agenda Jost Schatzmann ...

    mi.eng.cam.ac.uk/~sjy/papers/scty07.pdf
    20 Feb 2018: U| 103 and |M| 103. (24). Goals are composed ofNC constraints taken from theset of constraintsC, andNR requests taken from the setof requestsR.

  11. main.dvi

    mi.eng.cam.ac.uk/~sjy/papers/ywss05.pdf
    20 Feb 2018: At this point, the dialog state probabilities given by equation 24 are omputed. ... P1. find P2. (a) task 1.0. P1 b=1.0. b=0.7. b=0.3. b=0.24.

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