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1 - 10 of 11 search results for KaKaoTalk:ZA31 24 24 |u:mi.eng.cam.ac.uk where 0 match all words and 11 match some words.

  1. Results that match 2 of 3 words

  2. Infinite Support Vector Machines in Speech Recognition Jingzhou Yang, …

    mi.eng.cam.ac.uk/~mjfg/yang_is2013.pdf
    13 Jun 2013: Better performance couldbe achieved by gradually increasing C. Equation (16) is also known as the training criterion ofthe structural SVM [23, 24]. ... ACM, 2004. [24] Shi-Xiong Zhang and Mark Gales, “Structured SVMs for auto-matic speech

  3. A HIGH-PERFORMANCE CANTONESE KEYWORD SEARCH SYSTEM

    mi.eng.cam.ac.uk/~mjfg/ICASSP13_ibm2.pdf
    13 Jun 2013: with 24% speaking the Central Guangdong, 20%the Northern Pearl River Delta, 19% the Southern Pearl River Delta,19% the Guangxi and Western Guangdong, and 18% the NorthernGuangdong dialects. ... System combination is performed using an

  4. KERNELIZED LOG LINEAR MODELS FOR CONTINUOUS SPEECH RECOGNITION…

    mi.eng.cam.ac.uk/~mjfg/Kernel/sxz20_icassp13.pdf
    27 Mar 2013: 8 1.710 4.4 3.6 3.5 3.3 3.205 11.2 9.2 8.9 8.8 8.400 29.6 25.1 24.9 24.1 ... Speech Lang.,vol. 24, no. 4, pp. 648–662, 2010. [21] H. Liao and M.

  5. SYSTEM COMBINATION AND SCORE NORMALIZATION FOR SPOKEN TERM DETECTION

    mi.eng.cam.ac.uk/~mjfg/ICASSP13_ibm1.pdf
    13 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

  6. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/wang_icassp13.pdf
    13 Jun 2013: 2011, pp. 24–29, Ieee. [13] M. J. F. Gales and F.

  7. draft21.dvi

    mi.eng.cam.ac.uk/~mjfg/ASRU13.pdf
    7 Nov 2013: procedure described in [24]. Unilingual and multilingual AMs were. each built from a flat start. ... 24] J. Park et al., “The Efficient Incorporation of MLP Fea-.

  8. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/wang_is2012.pdf
    13 Jun 2013: RVTSJ 14.9 29.6 18.3 43.6 26.6RAT RVTSJ 13.7 28.5 15.0 42.2 24.9.

  9. EFFICIENT DECODING WITH GENERATIVE SCORE-SPACESUSING THE EXPECTATION…

    mi.eng.cam.ac.uk/~mjfg/Kernel/van_dalen-2013-efficient_decoding.pdf
    27 Mar 2013: 0s,0. 01s,0ei:o1/.96. 01s,1. ei:o1/.24. 02s,1t:o2/.4. 02s,2. t:o2/.4. 03s,1t:o3/.5. 03s,2. t:o3/.5. ... 24, no. 4, pp. 648–662, 2010. [6] A. Ragni and M.

  10. Department of Engineering 1 Generative Kernels and Score-Spaces…

    mi.eng.cam.ac.uk/~mjfg/Kernel/rcv25_2013_y2.pdf
    9 Sep 2013: w(r′)). (24). Normally, the results of the kernel function applied to each pair of training data pointsare used as entries of the Gram matrixG. ... 3.2.1 Form of kernel. e general form of the kernel in (24) relates two utterances and corresponding

  11. IB-interestpoints.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/old/2013-IB-handout2.pdf
    10 May 2013: 24 Engineering Part IB: Paper 8 Image Matching. Interest Point Detection - Blobs.

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