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  1. Results that match 1 of 2 words

  2. 12 Jul 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].
  3. 20 Feb 2018: In Proceedings of ACL, 2017. [24] Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, DavidVandyke, Tsung-Hsien Wen, and Steve Young. ... Computer Speech & Language, 24(2):150–174, 2010. [53] Steve Young, Milica
  4. yeyo06.dvi

    mi.eng.cam.ac.uk/~sjy/papers/yeyo06.pdf
    20 Feb 2018: g(i)jq =. T. t=1. v(t)ii d. (t)jq j, q = 1, , (d 1) (24). ... Markel,”Distance measures for speech processing”,IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.ASSP-24, no.5, pp.380-391, October 1976.
  5. 27 Jul 2020: CBCT non-planarity 2.47 2.46 3.35 15.7 16.0 14.8 12.1 10.8 8.63reach 9.36 9.16 9.57 26.7 24.7 ... MDCT non-planarity 3.04 3.67 4.52 15.1 14.2 15.5 11.7 10.6 8.3reach 13.6 11.2 10.4 26.3 24.1
  6. 20 Feb 2018: Surprised Sad Angry. wlex 13.67 12.30 18.74wwpos 24.52 11.29 18.47wspos 11.33 4.91 3.31wpofs 1.13 4.82 8.82wppofs 24.27
  7. 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.
  8. 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.
  9. 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.
  10. 20 Feb 2018: 24, no. 4, pp. 562–588, 2010. [3] R. Sutton and A.
  11. 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
  12. 4F12-notes-4.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/4F12/Slides/4F12-notes-4.pdf
    2 Nov 2023: 12 p. ′13 p. ′14. p′21 p′22 p. ′23 p. ′24. ... vp24)p22. p′24. . . . . . Z. . . .
  13. 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
  14. 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.
  15. 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].
  16. 20 Feb 2018: The COMMUNICATOR systems in contrast onlyrequest between 24% and 43% of the unknown slots in each state.
  17. 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.
  18. 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.
  19. 20 Feb 2018: The Pietquin model. Train TestPrecision Recall Precision Recall. BIG 19.74 24.11 17.83 21.66LEV 43.11 35.07 37.98 31.57PTQ 45.00 36.35 40.16
  20. Unsupervised Language Model Adaptation for Mandarin…

    mi.eng.cam.ac.uk/reports/svr-ftp/mrva_icslp06.pdf
    20 Jan 2007: Test set baseline N-gram adaptfixed weights dynamic weights. dev05bcm (BC) 25.6 24.5eval04 (BN) 14.7 14.8dev04f (BN) 6.4 6.5. ... P3 27.4 25.6 24.5 24.3 24.3. Table 3: P2, P3 stage dev05bcm CERs.
  21. 3_2_ransac

    mi.eng.cam.ac.uk/~cipolla/lectures/4F12/Slides/4F12-ImageStitching.pdf
    27 Oct 2020: x̃2 = [ R2 | 0 ] X̃ = R2X (A.24).

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