Search

Search Funnelback University

Search powered by Funnelback
10 results 20 results 50 results
Relevance Date (Newest first) Date (Oldest first) Title (A-Z) Title (Z-A) Distance URL (A-Z) URL (Z-A) Shuffle
Did you mean pc524 |u:mi.eng.cam.ac.uk?
61 - 70 of 1,000 search results for :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. 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

  4. 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].

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. A Network-based End-to-End Trainable Task-oriented Dialogue System…

    mi.eng.cam.ac.uk/~sjy/papers/wgmv17.pdf
    20 Feb 2018: 50.00 24.39 25.61Performance 43.90 25.61 30.49.

  10. IEEE SIGNAL PROCESSING MAGAZINE [128] MAY 2010 This article ...

    mi.eng.cam.ac.uk/~sjy/papers/youn10.pdf
    20 Feb 2018: THE HIDDEN INFORMATION STATE SYSTEMA block diagram of the HIS system is shown in Figure 13 [23], [24]. ... The history states record grounding and query status information but the details are not relevant here (see [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.

Refine your results

Date

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.