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11 Jan 2008: ML MPE ML MPE. S0 15.1 13.6 29.3 25.40.00 13.8 12.0 27.7 24.80.80 13.5 11.7 26.9 ... Conf. bnmdev06 bcmdev05Thresh. ML MPE ML MPE. 0.00 13.8 12.0 27.7 24.80.76 13.6 11.8 27.5 24.30.80 13.5 11.7

13 Nov 2008: IEEE Transactions on MedicalImaging, 23:153–163, 2004. [24] A. Pesavento, C. Perrey, M. ... Ultrasound in Medicine and Biology, 24:427–441, 1998. [31] R. Zahiri-Azar and S.

15 Aug 2008: Cambridge UniversityEngineering Department. August 2008 24. Instantaneous and Discriminative Adaptation for Automatic Speech Recognition. ... Adaptation Supervision1-Best Lattice Reference. — 29.2 — —. MLLR 27.0 26.7 24.3MLLRDMT 26.2 25.9 23.4.

6 Oct 2008: Cambridge UniversityEngineering Department. 24. Model-Based Approaches to Robust Speech Recognition. HTK-Based VTS and SVM Rescoring Performance. ... Comp IterWER%. ENON CITY HWY. VTS0 3.35 8.87 13.111 1.24 3.09 3.782 1.37 2.65 3.15.

13 Jun 2008: Cambridge UniversityEngineering Department. King’s College London Seminar 24. Model-Based Approaches to Robust Speech Recognition. ... p(Y; λ(1)). )λ(2) log. (p(Y; λ(2)). ). . – Related to the Fisher kernel [24].

11 Jan 2008: CNN 7.24 12.73 20.48 224.2. Table 5. Model size and text source for English component LMs. ... Adapt Int InitTER%. bnmd06 bcmd05 eval06fixed lin - 72.24 75.28 80.46.

16 Jun 2008: 20 I. 2. –24. yst1yst. yst1. 35 = Dyet (13). where D is the dynamic coefficient matrix and yet is the vectorof static coefficients in the appropriate window.

13 Aug 2008: In common with other work in this. 5. area [9, 24], G is approximated by the diagonalised empirical covariance matrix of the trainingdata. ... 00 25.48 21.73 25.95 24.46 21.64 26.05 24.51 22.56.

6 Nov 2008: In common with other work in this. 5. area [9, 24], G is approximated by the diagonalised empirical covariance matrix of the trainingdata. ... 00 25.48 21.73 25.95 24.46 21.64 26.05 24.51 22.56.

16 Feb 2008: 20. 21. 22. 23. 24. 25. Coverage. eval02eval03eval04. 1 2 3 4 523.2. ... 23.4. 23.6. 23.8. 24. 24.2. 24.4. 24.6. 24.8. 25. 25.2. BLEU.

16 Feb 2008: Code Breaking for Automatic Speech Recognition 24 |35). < > -. Studying Segment Set Pruning. Towards studying the ability of lattice pinching in.

16 Feb 2008: Minimum Bayes-Risk Classifiers for Machine Translation. Conclusions and Future Work. MBR Techniques in Automatic Speech Recognition and Machine Translation – p.24/33.

11 Jan 2008: γm(t)ot. )(24). γm(t) is the component posterior derived from P (θ|O, H, λ̂k1).For the frame independent assumption it is necessary to obtain. ... where G and k are the standard sufficient statistics given in equa-tions 23 and 24, except that γm(t)

26 Mar 2008: CNN 7.24 12.73 20.48 224.2. Table 5. Model size and text source for English component LMs. ... Adapt Int InitTER%. bnmd06 bcmd05 eval06fixed lin - 72.24 75.28 80.46.

17 Jan 2008: Among several techniques for displacement estimation [12, 24], correlation maximization wasthe first to be proposed and remains the most widely used [16]. ... As p1 is the only point ready for processing,it is selected and fed into any suitable

5 Sep 2008: H. Berman. Engineering a freehand 3D ultrasoundsystem. Pattern Recognition Letters, 24:757–777, 2003. ... Ultrasound in Medicine & Biology, 24(6):855–869, 1998. F. Rousseau, P. Hellier, and C.

16 Feb 2008: 32. 34. 36. 38. 40. 42. 44. 8 10 12 14 16 18 20 22 24. ... Lambert Mathias 24 / 43. MotivationProblem Definition. Discriminative Objective functionGrowth Transformations for MT.

3 Dec 2008: SI — MLLR 24.3 27.0MLLR MLLR 23.6 26.4. DSAT DLT DLT 18.4 28.1MLLRDMT MLLRDMT 22.5 25.3.

5 Sep 2008: µPRA = pW TWS TSI TspI′. (24). This is a measurement of system accuracy, and includes errors from every component of thesystem.

6 Mar 2008: The. 6. 20 40 60 80 100sample number. ry=24 a. ry=44 a. ... a). 20 40 60 80 100sample number. ry=24 a. ry=44 a.

16 Feb 2008: Bitext Alignment forStatistical Machine Translation. Yonggang Deng. A dissertation submitted to the Johns Hopkins University in conformity with the. requirements for the degree of Doctor of Philosophy. Baltimore, Maryland. 2005. Copyright c 2005 by

16 Jan 2008: 2.2.2 Hidden conditional random fields (HCRFs). 24. 2.2.3 Parameter estimation for conditional models.

19 Mar 2008: Foundations and Trends R inSignal ProcessingVol. 1, No. 3 (2007) 195–304c 2008 M. Gales and S. YoungDOI: 10.1561/2000000004. The Application of Hidden Markov Modelsin Speech Recognition. Mark Gales1 and Steve Young2. 1 Cambridge University

16 Feb 2008: 21.9 7.91 0.70 46.46 68.494 24.2 7.56 0.75 47.84 70.265 19.2 7.25 0.72 49.56 71.186

16 Feb 2008: 232.8.4 Phrase Extraction from a Lattice. 24. 2.9 ASR Lattice Pruning for Translation. ... or word lattices [24, 25]. N-Best translation is straightforward: a text-based SMT.

16 Feb 2008: 233.3.3 Obtaining Confusions from a Lattice. 24. 3.4 Obtaining Relevant Training data for the Binary Classifiers.

17 Sep 2008: Uncertainty Decoding forNoise Robust Speech Recognition. Hank Liao. Sidney Sussex CollegeUniversity of Cambridge. September 2007. This dissertation is submitted for the degree ofDoctor of Philosophy to the University of Cambridge. Declaration. This

9 Jul 2008: 232.5 Language Modelling. 242.6 Decoding. 24. 2.6.1 Viterbi Decoding. 252.6.2 Minimum Bayes’ Risk Decoding.

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