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AUTOMATIC COMPLEXITY CONTROL FOR HLDA SYSTEMS X. Liu, M. ...
mi.eng.cam.ac.uk/reports/svr-ftp/liu_icassp2003.pdf19 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 -
4F12-notes-1.dvi
mi.eng.cam.ac.uk/~cipolla/lectures/4F12/Slides/4F12-notes-1.pdf29 Sep 2023: CCD. • A typical digital SLR CCD measures about 24 16 mm. ... 24 Engineering Part IIB: 4F12 Computer Vision. Further reading. Students looking for a deeper understanding of computer vision might wish to con-sult the following publications, many of -
More Robust Schema-Guided Dialogue State Tracking via…
mi.eng.cam.ac.uk/~wjb31/eacl_2023_CR.pdf1 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 -
Structured Discriminative Models Using Deep Neural-Network Features
mi.eng.cam.ac.uk/~mjfg/asru15-vanDalen.pdf11 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]. -
N-BEST ERROR SIMULATION FOR TRAINING SPOKEN DIALOGUE SYSTEMS Blaise…
mi.eng.cam.ac.uk/~sjy/papers/thgt12.pdf20 Feb 2018: 24, no. 4, pp. 562–588, 2010. [3] R. Sutton and A. -
EFFECTS OF THE USER MODEL ON SIMULATION-BASEDLEARNING OF DIALOGUE ...
mi.eng.cam.ac.uk/~sjy/papers/swsy05.pdf20 Feb 2018: The COMMUNICATOR systems in contrast onlyrequest between 24% and 43% of the unknown slots in each state. -
Towards Learning Orientated Assessment for Non-native Learner Spoken…
mi.eng.cam.ac.uk/~kmk/presentations/ALTA_Sheffield_20190306.pdf8 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. -
Towards Learning Orientated Assessment for Non-native Learner Spoken…
mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/ALTA_Sheffield_20190306.pdf21 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. -
SYSTEM COMBINATION AND SCORE NORMALIZATION FOR SPOKEN TERM DETECTION
mi.eng.cam.ac.uk/~mjfg/ICASSP13_ibm1.pdf13 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 -
lect1.dvi
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect1.pdf10 Nov 2015: for minimum error with generative models. 24 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.
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