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
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. Overview - 2007

    mi.eng.cam.ac.uk/~cipolla/archive/Presentations/2007-Cipolla-Vision.pdf
    19 May 2014: 1) (2) (3) (4). (9)(8)(7)(6). (5). (24). Learned contour and texture.

  3. slides.dvi

    mi.eng.cam.ac.uk/~mjfg/Bilbao14/talk.pdf
    25 Jun 2014: Cambridge University. Engineering DepartmenteNTERFACE June 2014 24. Controllable and Adaptable Statistical Parametric Speech Synthesis Systems. ... Integrated Expressive Speech Training [24]. Training. Expressive State. Prediction. ExtractionAcoustic

  4. Knill_IS14_1.dvi

    mi.eng.cam.ac.uk/~ar527/knill_is2014a.pdf
    10 Nov 2014: ware. Speaker adaptive training (SAT) using CMLLR [24] is. applied in training and test, with MLLR also used for decoding. ... Makhoul, “Acompact model for speaker adaptive training,” in Proc. ICSLP,1996. [24] M.

  5. ICASSP2014b.dvi

    mi.eng.cam.ac.uk/~mjfg/yoshioka_ICASSP14.pdf
    3 Apr 2014: Workshop. Automat. SpeechRecognition, Understanding, 2011, pp. 24–29. [13] O. Abdel-Hamid and H. ... Mag.,vol. 29, no. 6, pp. 114–126, 2012. [24] T. Yoshioka, X.

  6. Cambridge University Engineering Department_ News item

    mi.eng.cam.ac.uk/~cipolla/archive/Public-Understanding/2005-CUED-Tracking-Crowds.pdf
    7 Nov 2014: Detecting and tracking individual people in a crowd. 24 August 2005.

  7. Combining Tandem and Hybrid Systems for Improved Speech…

    mi.eng.cam.ac.uk/~ar527/rath_is2014a.pdf
    10 Nov 2014: A speaker adaptive training (SAT) system using global con-strained maximum likelihood linear regression (CMLLR) ata speaker level [24] was then constructed, followed by bothMinimum Phone Error (MPE) [25] and ... 7, no. 3, pp. 272–281, May 1999. [24] M.

  8. EFFICIENT LATTICE RESCORING USINGRECURRENT NEURAL NETWORK LANGUAGE…

    mi.eng.cam.ac.uk/~mjfg/xl207_ICASSP14a.pdf
    28 Apr 2014: Instead, previous research has beenfocused on using N-best list rescoring for RNNLM performanceevaluation [13, 14, 26, 27, 24]. ... 21, no. 3, pp. 492–518,2007. [24] Y. Si, Q. Zhang, T.

  9. Combining Tandem and Hybrid Systems for Improved Speech…

    mi.eng.cam.ac.uk/~mjfg/interspeech14-rath.pdf
    23 Jun 2014: A speaker adaptive training (SAT) system using global con-strained maximum likelihood linear regression (CMLLR) ata speaker level [24] was then constructed, followed by bothMinimum Phone Error (MPE) [25] and ... 7, no. 3, pp. 272–281, May 1999. [24] M.

  10. Knill_IS14_final.dvi

    mi.eng.cam.ac.uk/~mjfg/interspeech14-knill.pdf
    23 Jun 2014: domised at the frame level across all the languages [24, 5]. ... 29, no. 6, pp. 82–97, Nov 2012. [24] J.-T. Huang et al., “Cross-language knowledge transfer using mul-tilingual deep neural network with shared hidden layers,” in Proc.ICASSP,

  11. unsup.eps

    mi.eng.cam.ac.uk/~ar527/ragni_is2014a.pdf
    20 Jun 2014: Thusmany of the waveform production issues [23] are not rele-vant. Furthermore, these schemes permit model-based adap-tation/compensation approaches [24] to be used for synthesis-ing data with target ... 51, pp. 1039–1064, 2009. [24] M. J. F. Gales,

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.