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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. A LANGUAGE SPACE REPRESENTATION FOR SPEECH RECOGNITION

    mi.eng.cam.ac.uk/~mjfg/icassp15-ragni.pdf
    18 May 2015: There are many options to select the form of rep-resentation of the clusters and the combination method to employ[18, 17, 13, 24, 25]. ... 20, no. 6, pp. 1713–1724, 2012. [24] V. Diakoloukas and V.

  5. 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.

  6. 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].

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. Use of Deep Learning in Free Speaking Non-native English Assessment

    mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/TSD2021_Knill.pdf
    21 Feb 2022: 24/56. Deep Density Network-based Grader [1, 7]. • Deep Density Networks predict parameters of a distribution.

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