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41 - 50 of 1,000 search results for :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. IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, JANUARY…

    mi.eng.cam.ac.uk/~sjy/papers/gayo14.pdf
    20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24]

  3. Phrase-based Statistical Language Generation usingGraphical Models…

    mi.eng.cam.ac.uk/~sjy/papers/mgjk10.pdf
    20 Feb 2018: Com-puter Speech & Language, 24(4):562–588, 2010. Y. Tokuda, T. Yoshimura, T. ... Computer Speech and Language,24(2):150–174, 2010.

  4. Bayesian update of dialogue state: A POMDP framework for spoken…

    mi.eng.cam.ac.uk/~sjy/papers/thyo10.pdf
    20 Feb 2018: 564 B. Thomson, S. Young / Computer Speech and Language 24 (2010) 562–588. ... B. Thomson, S. Young / Computer Speech and Language 24 (2010) 562–588 565.

  5. crosseval_diff-reward2b.ps

    mi.eng.cam.ac.uk/~sjy/papers/kgjm10.pdf
    20 Feb 2018: Yu. 2009. The Hidden InformationState model: a practical framework for POMDPbased spoken dialogue management.ComputerSpeech and Language, 24(2):150–174.

  6. On-line Active Reward Learning for Policy Optimisationin Spoken…

    mi.eng.cam.ac.uk/~sjy/papers/sgmb16.pdf
    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.

  7. DISCRIMINATIVE SPOKEN LANGUAGE UNDERSTANDINGUSING WORD CONFUSION…

    mi.eng.cam.ac.uk/~sjy/papers/hgtt12.pdf
    20 Feb 2018: 24, no. 4, Oct. 2010. [16] S. J. Young, G. Evermann, M.

  8. A Benchmarking Environment for ReinforcementLearning Based Task…

    mi.eng.cam.ac.uk/~sjy/papers/cbsm17.pdf
    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

  9. From Discontinuous To Continuous F0 Modelling In HMM-based…

    mi.eng.cam.ac.uk/~sjy/papers/yuty10.pdf
    20 Feb 2018: The feature set includes 24 spectralcoefficients, log F0 and 5 aperiodic component features.

  10. 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.

  11. STRUCTURED DISCRIMINATIVE MODELS USING DEEP NEURAL-NETWORK FEATURES…

    mi.eng.cam.ac.uk/~mjfg/vandalen_ASRU15.pdf
    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].

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