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11 - 20 of 24 search results for katalk:PC53 24 / |u:mi.eng.cam.ac.uk where 0 match all words and 24 match some words.

  1. Results that match 1 of 2 words

  2. Modular Construction of Complex Deep Learning Architectures in HTK

    mi.eng.cam.ac.uk/UKSpeech2017/posters/f_kreyssig.pdf
    20 Nov 2017: I All models used 24 log-Mel filter bank coefficients with their and values as input features, except the CNN which used40 without any.

  3. A learned emotion space for emotion recognition and emotive speech…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/z_hodari_poster.pdf
    23 Dec 2017: Table 1: Performance classifying; happy, sad, angry, neutral. Model Inputs AccuracyRandom N/A 24.14%Most common N/A 33.00%LSTM eGeMAPS LLDs 43.17%TD-CNN Spectrogram

  4. Template.dvi

    mi.eng.cam.ac.uk/~mjfg/CUED-Chen-RNNLMKWS.pdf
    22 Mar 2017: Mongolian FLP 511K 24.0K - 4.19 12.19WEB 139M 199.8K 0.93 2.10 5.62. ... 24,no. 11, pp. 2146–2157, 2016. [15] Xie Chen, Yongqiang Wang, Xunying Liu, Mark Gales, andP.

  5. Low-Resource Speech Recognition and Keyword-Spotting

    mi.eng.cam.ac.uk/~mjfg/SPECOM_2017.pdf
    29 Nov 2017: 23/63. Stimulated Systems. /ey//em/. /sil/. /sh/. /ow/ /ay/. 24/63. Stimulated Network Training. •

  6. MORPH-TO-WORD TRANSDUCTION FOR ACCURATE AND EFFICIENT AUTOMATICSPEECH …

    mi.eng.cam.ac.uk/~mjfg/CUED-Ragni-Morph-To-Word.pdf
    22 Mar 2017: FLP Web FLP Web (#) ASR KWSSwahili 294 – 24.4 0 8.2 8.5 19.6Dholuo 467 1,217 17.5 18.8 6.1 3.0 10.0Amharic 388 ... 4, pp. 1738–1752, 1990. [24] P. Ghahremani, B. BabaAli, D. Povey, K.

  7. STIMULATED TRAINING FOR AUTOMATIC SPEECH RECOGNITION ANDKEYWORD…

    mi.eng.cam.ac.uk/~mjfg/CUED-Ragni-Stimulated-ASR-KWS.pdf
    22 Mar 2017: These weretrained on FLP data of 24 Babel languages and CTS data of 4 addi-tional languages, English, Spanish, Arabic and Mandarin, releasedby LDC. ... Stacked Hybrids were trained withand without stimulated training using monophone initialisation

  8. 1 Statistical Sequence ModellingMark Gales Speech recognition and…

    mi.eng.cam.ac.uk/~mjfg/sequence17-draft.pdf
    24 May 2017: Tt=1. p(yt|ht,h̃t) (1.24). where the normalisation term ensures that this is valid PDF. ... 24. Fcml(λ;D) =n. i=1. log( p(w(i)1:L(i)|Y(i). 1:T (i) ; λ)) (1.100).

  9. How Does the Femoral Cortex Depend onBone Shape? A ...

    mi.eng.cam.ac.uk/reports/svr-ftp/gee_tr704.pdf
    15 Jun 2017: How Does the Femoral Cortex Depend onBone Shape? A Methodology for the Joint. Analysis of Surface Texture and Shape. A. H. Gee, G. M. Treece and K. E. S. Poole. CUED/F-INFENG/TR 70415 June 2017. Cambridge University Engineering DepartmentTrumpington

  10. University of CambridgeEngineering Part IB Information Engineering…

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/old/2017-DNN-lecture-3.pdf
    18 May 2017: the 9 filters create 9 images, which have 9 24 24 = 5184pixels, and thus we need 51,840 parameters to reduce those.

  11. IB-interestpoints.dvi

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/old/2017-IB-handout2.pdf
    18 May 2017: outliers in the output of the corner detector. 24 Engineering Part IB: Paper 8 Image Matching.

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