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  1. Results that match 1 of 2 words

  2. 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.
  3. 20 Feb 2018: Thomson and Young2010] Blaise Thomson and SteveYoung. 2010. Bayesian update of dialogue state:A pomdp framework for spoken dialogue systems.Computer Speech and Language, 24:562–588.
  4. 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
  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. 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.
  7. 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.
  8. 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.
  9. 20 Feb 2018: 24, no. 4, pp. 562–588, 2010. [3] R. Sutton and A.
  10. 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
  11. 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].

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