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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.

19 May 2014: 1) (2) (3) (4). (9)(8)(7)(6). (5). (24). Learned contour and texture.

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.

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

7 Nov 2014: Detecting and tracking individual people in a crowd. 24 August 2005.

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.

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.

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.

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,

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,

23 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,

7 Nov 2014: BBC News 24. News services Your news when youwant it. News Front Page.

6 Jun 2014: This is effectively anunsupervised acoustic model training process [24, 25]. For all experiments the core ASR toolkit, used for acousticfeature generation, clustering, decoding and GMM-based acousticmodel training, was an extended ... 7319–7323, IEEE,

10 Nov 2014: This is effectively anunsupervised acoustic model training process [24, 25]. For all experiments the core ASR toolkit, used for acousticfeature generation, clustering, decoding and GMM-based acousticmodel training, was an extended ... 7319–7323, IEEE,

4 Aug 2014: 0.3 t < 1.0 1.06 0.37 2.35 1.46 0.58 1.07 0.24 0.14 0.15 0.23 0.04 0.31. ... 0.3 t < 1.0 2 31 24 60 2 35 3 30 3 30 3 30.

7 Nov 2014: OM. Y. CAMBRIDGE FUTURE. 24. AtomsWe can now distinguish singleatoms in some materials, formingimages at around 100 milliontimes magnification using power-ful electron microscopes.

7 Nov 2014: Video: Ninja cat - beware if this light-footed kitty isaround (London 24).

7 Nov 2014: He. Secondary schoolsComplete rankings: GCSEand A-level results. 24/08/2009 16:45Do the maths - Times Online. ... At Langley, Professor Cipolla had asked the class of 24 howmany wanted to be scientists after university.

2 Apr 2014: 2.3.1 Unit selection. 22. 2.3.2 Composite HMMs. 24. 2.4 Phonetic decision trees. ... 23. 2.5 Composite HMM. 24. 2.6 Phonetic decision tree. 25. 2.7 Tree-intersect model.

19 May 2014: There exists a large body of work. on this problem [8–10, 14, 16, 20, 24, 32], the majority of. ... Interna-tional Conference on Image Processing 1, 481–484 (2000). 24. Marsland, S., Twining, C., Taylor, C.: Groupwise non-rigid registration using