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22 Feb 2007: LanguageTraining. ML MPE. English (WER%) 16.0 13.1Arabic (WER%) 22.9 20.0. Mandarin (CER%) 14.4 12.7. ... 3-emitting state HMMs. ClassifierTraining Componentsλ α 10 20. HMM ML – 29.4 27.3C-Aug ML CML 24.2 –. HMM MMI – 25.3 24.8C-Aug MMI CML 23.4

15 Aug 2007: Compensation Noise Est. Type %WER. None — 20.8. JointML VTS 19.1ML Joint 18.8. ... The best training scheme was. the JAT system, which exceeded matched and multistyle with VTSperformance at both 20 and 14 dB SNR.

22 Jun 2007: Two thresholds were used in the show level selection. The firstwas a show-level confidence score of 55%, the second a thresholdof 20% for the percentage of English. ... The gains from using discriminative training weremuch reduced. MPE unsupervised

22 Jun 2007: In addition a maximum segmentlength of 20 seconds was imposed by splitting longer segments atthe longest inter-word gap. ... System Comb. CER%level bnmd06 bcmdr06CU — 8.0 21.8BBN 8.6 23.9BBNCU — 7.5 20.7CUBBN char 7.1 21.3.

15 Aug 2007: Here. Σ(m)-1 = A(r)TΣ̃(m)-1diag A. (r) (20). and Σ̃(m)diag is a diagonal covariance matrix. ... System # iter SNR(dB)20 15 10 50 31.2 50.4 79.2 89.7.

22 Feb 2007: m=1. γjm(t) (20). For the second derivative,. ckn Tcjm log(p(O1:T ; λ)) = (21)1. ... model. No data or feature whitening was performed. Classifier Criterion Componentsλ α 10 20.

10 Oct 2007: Two thresholds were used in the show level selection. The firstwas a show-level confidence score of 55%, the second a thresholdof 20% for the percentage of English. ... The gains from using discriminative training weremuch reduced. MPE unsupervised

20 Jan 2007: BN data and 20 hours of BC data. The language model is describedin section 3.2.

10 Oct 2007: In addition a maximum segmentlength of 20 seconds was imposed by splitting longer segments atthe longest inter-word gap. ... System Comb. CER%level bnmd06 bcmdr06CU — 8.0 21.8BBN 8.6 23.9BBNCU — 7.5 20.7CUBBN char 7.1 21.3.

30 May 2007: Tl(w) = log (P (w)) (20). and constrain all the α’s associated with this for all models tobe the same, αl. ... model. No data or feature whitening was performed. Classifier Criterion Componentsλ α 10 20.

4 Apr 2007: If eigenvoices are updated using the maximumlikelihood eigenspace (MLES) approach [20], it is equivalentto updating CAT cluster mean matrices and leaving covariancematrices unchanged [5]. ... Int. Conf. Speech Language Processing, 1998, pp.1771–1774.

10 Oct 2007: µm =xnm xdm Dµ̂mβnm βdm Dm. (20). Wmpem =Ynm Ydm Dm(Σ̂m µ̂mµ̂′m). ... Combining equations (20)and (21) gives the full covariance statistics in terms ofDm,.

30 Mar 2007: 0 20 40 60 80 100 120 1400.5. 0. 0.5. 1. ... es). 0 20 40 60 80 100 120 1400.5. 0. 0.5.

5 Oct 2007: 0.4. peak. offs. et (. mm. ). depth (mm)0 10 20 30 40. ... IEEETransactions on Ultrasonics, Ferroelectrics, and Frequency Control, 33(3):257–264, May 1986. [20] G.

16 Aug 2007: Wecanadvectdt(u, v) to estimate(ut, vt) using the followingequation from [20]. (uj , vj ) = (uj1, vj1)dj1(. ... Thirdrow: texture-mapping is obtained by advecting frame-by-frame 2D optical flow [20].

22 Feb 2007: Typically for HTK (natural logs)– α in range 10 to 16– β in range 0 to 20. ... Cambridge UniversityEngineering Department. ICASSP 2006 Tutorial 20. Recent Progress in LVCSR: An HTK Perspective cMark Gales & Phil Woodland, 2006.

19 Jun 2007: We achieve this in a system which matches the fastestreported processing time of about 20 seconds (Anand et al., 2007), even though this technique onlyemployed 1D windows. ... 3D strain images were calculated in a postprocessing step, lasting typically

4 Apr 2007: This model will be referenced to aspMPE [20]. Similar to fMPE, the time dependent scale factor is a weightedcontribution from the centroid specific scales where the weights are given bythe posteriors ... 20. The fMPEMPE system improved the baseline by

3 May 2007: Bias transforms have been used for adaptation in speechrecognition, but have been shown to be less effective than more complex transforms [20]. ... PhD thesis, ICSI,UC Berkely, 1999. [20] L R Neumeyer, A Sankar, and V V Digalakis.

23 Apr 2007: Thus. q(θ, T ) = q(θ|O, H)q(T |O, H) (20). This assumption is necessary to obtain a tractable mathemati-cal form. ... The optimisation of the boundis then only based on possible hypotheses for the uth block.The variational distributions in equation (20)