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1 - 9 of 9 search results for TALK:ZA31 24 / |u:mi.eng.cam.ac.uk where 0 match all words and 9 match some words.

  1. Results that match 1 of 2 words

  2. UKspeech2017

    mi.eng.cam.ac.uk/UKSpeech2017/posters/y_wang.pdf
    17 Nov 2017: L1, proficiency level, recordingSpontaneous responses increase difficulty, e.g. disfluenciesTranscribing is challenging inter-annotator error rate about 24.7%.

  3. 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

  4. .poster_jeremy_v2.tex.dvi

    mi.eng.cam.ac.uk/UKSpeech2017/posters/j_wong.pdf
    17 Nov 2017: 207Vseparate 45.8 46.0 46.6MT 47.7 47.8 47.3MT-TS 45.7 45.7 46.3. AMIseparate 24.5 24.6 24.6MT 25.4 25.5 ... 25.1MT-TS 24.3 24.4 24.6.

  5. Automa(c Analysis of Mo(va(onal Interviewing with Diabetes Pa(ents…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/x_wei.pdf
    20 Nov 2017: Results:. Model Senone No. WER (%). Baseline DNN-‐HMM 3981 53.13. MI adapted DNN 3981 47.24. ... Hhit NF NREF PRC RCL. lium 35 144 93 0.24 0.38 ivector 42 159 93 0.26 0.45.

  6. Future Word Contexts in Neural Network Language Models

    mi.eng.cam.ac.uk/UKSpeech2017/posters/x_chen.pdf
    17 Nov 2017: dev evalwords (w/s) PPL. ng4 - - 80.4 23.8 24.2uni-rnn - 4.5K 66.8 21.7 22.1. ... ng4 - 23.8 23.5 24.2 23.9uni-rnn - 21.7 21.5 21.9 21.7.

  7. Experimental Studies on Teacher-student Training of Deep Neural…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/q_li.pdf
    20 Nov 2017: 22. 23. 24. 25. 26. 27. 28. 29. 30. PER. (%). 3-layer (100) Baseline3-layer (100) Student3-layer (250) Baseline3-layer (250) Student3-layer (500) Baseline3-layer (500) Student4-layer (500) ... PER (%)7-layer (500) 24.55 23.55RNN 23.84 20.59Ensemble 23.73

  8. Use of Graphemic Lexicons for Spoken Language Assessment

    mi.eng.cam.ac.uk/UKSpeech2017/posters/k_knill.pdf
    17 Nov 2017: Decoder Gujarati Mixed(word) %PER %GER %PER %GER. Ph 25.8 24.9 33.9 32.9Gr 29.0 23.7 36.6 30.8.

  9. 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.

  10. 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

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