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1 - 10 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. .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.

  3. Deep Learning for Speech Recognition

    mi.eng.cam.ac.uk/~mjfg/LxMLS17.pdf
    29 Nov 2017: Network Interpretation [24]. Standard /ay/ Stimulated /ay/. • Deep learning usually highly distributed - hard to interpret• awkward to adapt/understand/regularise• modify training - add stimulation regularisation• improves ASR performance.

  4. STIMULATED TRAINING FOR AUTOMATIC SPEECH RECOGNITION ANDKEYWORD…

    mi.eng.cam.ac.uk/~ar527/ragni_icassp2017b.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

  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. Template.dvi

    mi.eng.cam.ac.uk/~ar527/chen_icassp2017a.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.

  7. 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.

  8. 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

  9. 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%.

  10. 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.

  11. 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.

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