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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. More Robust Schema-Guided Dialogue State Tracking via…

    mi.eng.cam.ac.uk/~wjb31/eacl_2023_CR.pdf
    1 Mar 2023: lightweight data augmentation for lowresource slot filling and intent classification. In Pro-ceedings of the 34th Pacific Asia Conference on Lan-guage, Information and Computation, PACLIC 2020,Hanoi, Vietnam, October

  3. 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.

  4. 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.

  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. 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.

  8. 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.

  9. 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

  10. 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.

  11. SYSTEM COMBINATION AND SCORE NORMALIZATION FOR SPOKEN TERM DETECTION

    mi.eng.cam.ac.uk/~mjfg/ICASSP13_ibm1.pdf
    13 Jun 2013: Ney LM with optimized discounting parameters [24] usinga modified version of the RWTH open source decoder [25]; and (6)DBN, a deep belief network hybrid model [26, 27] with discrimi-native ... Ney, “Posterior-scaled mpe: Novel discriminative

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