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STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES…
mi.eng.cam.ac.uk/~mjfg/vandalen_ASRU15.pdf12 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]. -
BN-E Experiments in Cambridge Do Yeong Kim, Mark Gales, ...
mi.eng.cam.ac.uk/research/projects/EARS/pubs/kim_sttmar05.pdf12 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. -
Practicable Assessment of Cochlear Sizeand Shape from Clinical CT ...
mi.eng.cam.ac.uk/reports/svr-ftp/gee_tr004.pdf27 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 -
This article appeared in a journal published by Elsevier. ...
mi.eng.cam.ac.uk/~sjy/papers/inyo0920 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 -
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. -
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 -
A LANGUAGE SPACE REPRESENTATION FOR SPEECH RECOGNITION
mi.eng.cam.ac.uk/~mjfg/icassp15-ragni.pdf18 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. -
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. -
Applying Deep Learning in Non-native Spoken English Assessment
mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/APSIPA2019_Knill.pdf21 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. -
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].
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