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19 May 2014: Treat score as function of. position. -100 -80 -60 -40 -20 0 20 40 60 80 100-3. ... 100 Training. examples. 12 Relevant vectors. remain. 0 10 20 30 40 50 60 70 80 90 100-0.6.

25 Jun 2014: Cambridge University. Engineering DepartmenteNTERFACE June 2014 20. Controllable and Adaptable Statistical Parametric Speech Synthesis Systems. ... recognition in different environments [19]/speaker identification [20]. Cambridge University. Engineering

10 Nov 2014: from multiple languages [20]. Context dependent (CD) out-. put layer targets were adopted as they have been found to yield. ... 307–312. [20] G. Hinton, L. Deng et al., “Deep Neural Networks for AcousticModeling in Speech Recognition,” Signal

19 May 2014: 10 20 30 40 50 60. See. Hear. Fear of public speaking. • ... Arial and 32 pt. Arial and 28 pt. Arial and 20 pt.

28 Apr 2014: Avgtesta testb testc. HMM — 9.83 9.11 9.53 9.48SVM. Log-Like8.29 7.90 8.61 8.20. ... 20, no. 7, pp. 2149–2158, 2012. [5] Robert A. Jacobs, Michael I.

10 Nov 2014: The underlying context-dependent states were specifiedusing state [19, 20, 21], rather than phone-state, roots of thedecision tree. ... 20] S. J. Young, J. J. Odell, and P. C. Woodland, “Tree-basedstate tying for high accuracy acoustic modelling,” in

19 May 2014: 9.0 20.0 17.0. Pruning the Search-Tree. 9.0 20.0 17.0. Pruning the Search-Tree. ... 9.0 20.0 17.0. 10.0 4.5 6.7. Pruning the Search-Tree. 9.0 20.0 17.0.

3 Apr 2014: Audio, Speech, Language Process.,vol. 20, no. 1, pp. 30–42, 2012. [2] L. ... Int. Conf.Acoust., Speech, Signal Process., 2013, pp. 7063–7067. [20] M. J.

19 May 2014: Detecting frontal faces. 0 10 20 30 40 50 60 70 80.

28 Apr 2014: 3. HISTORY CONTEXT CLUSTERING FOR RNNLMS. Efficient use of language models in speech recognizers [20, 19, 17]requires that the context dependent states representing different his-tories during search can be ... In common with the n-gram history based