Search

Search Funnelback University

Search powered by Funnelback
10 results 20 results 50 results
Relevance Date (Newest first) Date (Oldest first) Title (A-Z) Title (Z-A) Distance URL (A-Z) URL (Z-A) Shuffle
11 - 20 of 29 search results for TALK:PC53 20 |u:mi.eng.cam.ac.uk where 0 match all words and 29 match some words.

  1. Results that match 1 of 2 words

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

    mi.eng.cam.ac.uk/UKSpeech2017/posters/q_li.pdf
    20 Nov 2017: PER (%)7-layer (500) 24.55 23.55RNN 23.84 20.59Ensemble 23.73 20.34. Table 1: RNN and ensemble teacher models with a 3-layer (500)fully-connected student

  3. UKspeech2017

    mi.eng.cam.ac.uk/UKSpeech2017/posters/y_wang.pdf
    17 Nov 2017: 0. 20. 40. 60. 80. 100. % o. f Utte. ranc.

  4. The University of Birmingham 2017 SLaTE CALL Shared Task Systems

    mi.eng.cam.ac.uk/UKSpeech2017/posters/m_qian.pdf
    20 Nov 2017: 50% vs. 20%. • PF-STAR German: German children aged 10-13, 3.38 hours of read speech.Acoustic Model. •

  5. Genigraphics Research Poster Template A0/A1

    mi.eng.cam.ac.uk/UKSpeech2017/posters/m_al-radhi.pdf
    17 Nov 2017: 20, no. 1, pp. 102-105, 2013. [4] T. Drugman and Y.

  6. An avatar-based system for identifying individuals likely to develop…

    mi.eng.cam.ac.uk/UKSpeech2017/posters/b_mirheidari.pdf
    17 Nov 2017: Features• Conversation Analysis inspired[3]: 20 features,. e.g. patient answered me for who’s most concernedquestion, average number of empty words (CA is anapproach to study social interaction/ communicationability of

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

    mi.eng.cam.ac.uk/UKSpeech2017/posters/z_hodari_poster.pdf
    23 Dec 2017: Non-emotive 5.845 0.329 52.846 14.768. Listening test. • MUSHRA listening test, 16 screens, 20 participants• Copy synthesis reference: 100 rating for all samples.

  8. Template.dvi

    mi.eng.cam.ac.uk/~mjfg/CUED-Chen-RNNLMKWS.pdf
    22 Mar 2017: keyword)is searched among all possible paths in lattices. The indexing andsearch of our KWS system are based on the weight finite state trans-ducer (WFST) framework [20, 21]. ... ICASSP, 2013. [20] Brian Kingsbury, Jia Cui, Xiaodong Cui, Mark Gales,

  9. University of CambridgeEngineering Part IB Information Engineering…

    mi.eng.cam.ac.uk/~cipolla/lectures/PartIB/old/2017-DNN-lecture-2.pdf
    18 May 2017: P (i) log Q(i). P0.000.05. 0.10. 0.15. 0.20. 0.25. 0.30. 0.35. ... 1.00E-01. 0 2 4 6 8 10 12 14 16 18 20.

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

    mi.eng.cam.ac.uk/~mjfg/CUED-Ragni-Morph-To-Word.pdf
    22 Mar 2017: Another option is to create a phone index usingeither the original morph index or created word index [20]. ... 20] L. Mangu, B. Kingsbury, H. Soltau, Kuo H.-K., andM. Picheny, “Efficient spoken term detection using confusionnetworks,” in ICASSP, 2014.

  11. STIMULATED TRAINING FOR AUTOMATIC SPEECH RECOGNITION ANDKEYWORD…

    mi.eng.cam.ac.uk/~mjfg/CUED-Ragni-Stimulated-ASR-KWS.pdf
    22 Mar 2017: Keyword search is performed using joint decod-ing lattices pruned to yield on average 20,000 arcs. ... 20] D. Palaz, M. Magimai.-Doss, and R. Collobert, “Analysis ofcnn-based speech recognition system using raw speech as in-put,” in Interspeech, 2015.

Refine your results

clear all

Date

Search history

Recently clicked results

Recently clicked results

Your click history is empty.

Recent searches

Recent searches

Your search history is empty.