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10 Nov 2016: ω1 :. [11. ] [22. ] [20. ]. ω2 :. [00. ]

12 May 2016: 10. 20. 30. 40. 50. A1 A2 B1 B2 C. %WER. ... System HL-dim Training Data. % Error. KNN - SUP 20.8 RNNLM 100 17.5 RNNLM 200 Semi-SUP 9.3.

24 Jun 2016: Unfortunately extracting fixed dimensional featuresfrom variable-length observation sequences and modelling thevast, unstructured, mostly unseen space of possible sentencesis non-trivial [20]. ... 20, no. 3, pp. 273–297, 1995. [20] P. Nguyen, G. Heigold

29 Nov 2016: 01. ] [. 20. ]. (c) There are multiple solutions for α (though a unique decision boundary) to thisas it is an under-specified problem.

10 Nov 2016: layer parameters. The latter includes augmentation schemes. [17, 18, 9, 10, 19, 20, 21, 22, 23, 24, 25, 26]. ... Data-based schemes instead. make use of data to initialise [27], train [20, 23] or adapt [24] the.

12 May 2016: Seminar at Edinburgh University February 2014 20. Multilingual STT for Spoken Term Detection.

26 Sep 2016: In order to setup the stimulated DNNs, the mono-phone 2Dpositions were firstly obtained via t-SNE [17] over the training-set averaged CMLLR [20] frames of the phonemes. ... Fall 2004 Rich Transcription Workshop (RT-04),2004. [20] M. J. Gales, “Maximum

12 May 2016: Babel ProgramSeminar at Cambridge University April 2014 20. Multilingual STT for Spoken Term Detection.

5 Apr 2016: 19] introduces a scaling factor on hidden-layer activationsand in [20], the differentiable pooling technique is used to obtainthe speaker-dependent compensation from a hidden-activation can-didate pool. ... 20, no. 1, pp.30–42, 2012. [2] Geoffrey

12 May 2016: 1520. 25. 0. 5. 10. 15. 20. 250. 0.05. 0.1. 0.15. ... and Language Processing, vol. 20, no. 1, pp. 30–42, 2012. [5] A.

12 Jul 2016: the Gaussian sufficient statistics[19] and HMM mean and variance statistics [20]. ... 1117–1120. [20] Georg Heigold, Ralf Schlüter, and Hermann Ney, “On theequivalence of Gaussian HMM and Gaussian HMM-like hid-den conditional random fields.,” in

5 Apr 2016: the Gaussian sufficient statistics[19] and HMM mean and variance statistics [20]. ... 1117–1120. [20] Georg Heigold, Ralf Schlüter, and Hermann Ney, “On theequivalence of Gaussian HMM and Gaussian HMM-like hid-den conditional random fields.,” in

12 May 2016: further developed by a number of sites [19, 20, 21, 22]. • ... Corpora,” in Proc. HLT-EMNLP, 2005. [20] George Saon, Hagen Soltau, Upendra Chaudhari, Stephen Chu, Brian Kingsbury, Hong-.

8 Nov 2016: The LSTM is then unrolled for 20timesteps, and thus consumes a larger context of 20 l. ... µ)m (x t ),F(σ)m (x t )). 34/68. Gradient Descent [20]. •

29 Sep 2016: 19 of 67. Long-Short Term Memory Networks [20, 16]t. xt. ht1. ... i. time delay. f. ii io. ht1x. 20 of 67. Long-Short Term Memory Networks. •

26 Sep 2016: tic modelling techniques. These individual systems might usedifferent front-ends, segmentations, dictionaries or decisiontrees [20, 21]. ... 20, no. 3, pp. 273–297, 1995. [23] P. Nguyen, G. Heigold, and G.

11 Mar 2016: For thisreason, RNNLMs are usually linearly interpolated with n-gram LMsto obtain both a good context coverage and strong generalisation [1,3, 17, 18, 19, 20]. ... ISCA Interspeech, 2010. [20] Hai-Son Le, Ilya Oparin, Alexandre Allauzen, J Gauvain,

26 Sep 2016: layer parameters. The latter includes augmentation schemes. [17, 18, 9, 10, 19, 20, 21, 22, 23, 24, 25, 26]. ... Data-based schemes instead. make use of data to initialise [27], train [20, 23] or adapt [24] the.

23 May 2016: All DNN models used in thispaper are hybrid models [20]. The IBM Attila speech recognitiontoolkit [42] is used for training the models presented in this paper. ... 20] Brian Kingsbury, Tara N Sainath, and Hagen Soltau, “Scalableminimum bayes risk

12 Jul 2016: 5.1. AURORA 4. AURORA 4 is a medium-to-large noise-corrupted speech recogni-tion task [20]. ... 6950–6954. [20] N. Parihar and J. Picone, “Aurora working group: DSR frontend LVCSR evaluation,” Tech.

11 Mar 2016: 5.1. AURORA 4. AURORA 4 is a medium-to-large noise-corrupted speech recogni-tion task [20]. ... 6950–6954. [20] N. Parihar and J. Picone, “Aurora working group: DSR frontend LVCSR evaluation,” Tech.

26 Jul 2016: 19. 2.4.1 Maximum a Posteriori (MAP). 20. 2.4.2 Linear Transform Based Adaptation. ... criterion (2.20) with 1/0 loss deûned in (2.21). • Word-level loss: his loss function is directly related to the expected word error rate.