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22 Sep 2015: en Tue, 22 Sep 2015 07:38:20 GMT https://getnikola.com/ http://blogs.law.harvard.edu/tech/rss EPSRC Funded Research Project

10 Nov 2015: 0.1 0.2 0.5 1 2 5 10 20 40. 0.1. 0.2. ... 0.5. 1. 2. 5. 10. 20. 40. False Alarm probability (in %). Miss. pro. babi. lity (i. n %. ). GMM SVM. MAP (τ)EER.

10 Nov 2015: 20. -10. 0. 10. 20. -10. 0. 10. 0. 0.01. 0.02. ... 10. 010. 20. -10. 0. 10. 20. 0. 0.005. 0.01. P.

10 Nov 2015: Bayes’ Rule is sometimes remembered as. posterior likelihood prior. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. ... 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. False Positive.

10 Nov 2015: Bayes’ Rule is sometimes remembered as. posterior likelihood prior. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. ... 10. 20. 30. 40. 50. 60. 70. 80. 90. 100. False Positive.

10 Nov 2015: 0.05. 0.1. 0.15. 0.2. 0.25. 0.3. 0.35. 20. 4 Engineering Part IIB: Module 4F10 Statistical Pattern Processing. ... 4. 2. 0. 2. 4. 6. 83 ITERATIONS. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.

10 Nov 2015: 05. 1015. 2025. 0. 5. 10. 15. 20. 250. 0.05. 0.1.

3 Jun 2015: 20, no. 1, pp. 14–22, 2012. [17] G. Hinton, L. Deng, D. ... 34, no. 1, pp. 25–40, 2001. [20] H. Misra, H. Bourlard, and V.

10 Nov 2015: 1. 1. x1. 1x2. 0.5NOR. (d) NOR operator. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.

25 Nov 2015: Classifier 1. Processing. 20. 4F10: Statistical Pattern Processing. Adaptive Directed Acyclic Graph.

18 May 2015: Though for speaker adaptation this may be reason-able [20], for language adaptation this is expected to be suboptimalas different eigen-languages may have different acoustic realisationsof context-dependencies. ... Rep. CUED/F-INFENG/TR263,Cambridge

6 Apr 2015: Though for speaker adaptation this may be reason-able [20], for language adaptation this is expected to be suboptimalas different eigen-languages may have different acoustic realisationsof context-dependencies. ... Rep. CUED/F-INFENG/TR263,Cambridge

21 Jan 2015: This was the approach adopted from the graphemicModern Standard Arabic (MSA) systems in [19] and the system forthe RATS data in [20]. ... 26, no. 4, pp. 229–243, 2012. [20] Mark J. F. Gales and Federico Flego, “Model-based ap-proaches for degraded

3 Jun 2015: The training data for the other languageswas retrieved from the Leipzig corpora [20]. ... 485–525. [19] B. Carpenter. Lingpipe dynamiclmclassifier. [Online]. Avail-able: http://alias-i.com/lingpipe/docs/api/com/aliasi/classify/DynamicLMClassifier.html.

10 Nov 2015: 1. 1. 1. 1. 1.5 AND. 25. 20 Engineering Part IIB: Module 4F10 Statistical Pattern Processing.

9 Oct 2015: Joint decoding can be viewed as a single multi-stream sys-tem [20, 21]. ... 20, no. 1, pp. 14–22, 2012. [18] G. Hinton, L. Deng, D.

3 Feb 2015: 5. 330. mean surface density (mg/cm )20 660. (a) Male mean CMSDp (vertex)p (cluster)% change per s.d. ... exploration 1 Hgt Age Wgt Shp Site Hgt 2.20 102 40 3.50n=308 1 S0 Age Wgt Shp Site S0 5.09 107 208 7.84males 1 S0 Age

18 May 2015: 20]. This expert derived set of decision tree ques-tions was compared with the unicode-based graphemic approach de-scribed in this paper. ... 26, no. 4, pp. 229–243, 2012. [20] M.J.F. Gales and F.

16 Jun 2015: Theimplementation of the incremental algorithm uses the author’sFlipsta C++ library for finite-state automata [20], open-sourcedunder the permissive Apache License [21], which allows morelazy operations. ... 101. 102. 103. Mem. ory. use. (MB. ). 0 10

9 Oct 2015: The training data for the other languageswas retrieved from the Leipzig corpora [20]. ... 485–525. [19] B. Carpenter. Lingpipe dynamiclmclassifier. [Online]. Avail-able: http://alias-i.com/lingpipe/docs/api/com/aliasi/classify/DynamicLMClassifier.html.