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INVESTIGATION OF ACOUSTIC MODELING TECHNIQUES FOR LVCSR SYSTEMS X. ...
mi.eng.cam.ac.uk/reports/svr-ftp/liu_icassp2005.pdf19 May 2005: Systemeval03. dev04s25 fsh Avg. P2-cn HLDA 26.6 18.4 22.6 18.7. P3a-cn SAT 24.5 17.1 20.9 17.3P3c-cn SPron 24.7 ... cn SPAM 24.1 16.9 20.6 17.2P3h-cn SATSPAM 23.9 16.9 20.5 16.8P3i-cn CTRL 24.5 17.5 21.1 17.6. -
AUTOMATIC COMPLEXITY CONTROL FOR HLDA SYSTEMS X. Liu, M. ...
mi.eng.cam.ac.uk/reports/svr-ftp/liu_icassp2003.pdf19 Sep 2003: Fig. 1. Test set word error rate for all possible models, with thestandard front-end 12, 16 and 24 component performance. ... The best perfor-mance, 36.8%, was obtained using 24 components per state and anHLDA projection from 52 dimensions to 38 -
Still Talking to Machines (Cognitively Speaking) Steve Young…
mi.eng.cam.ac.uk/~sjy/papers/youn10a.pdf20 Feb 2018: partition ex-plicitly records the fact that x = a and the existing partitionis updated to record the fact that x = ā [24]. -
DEVELOPMENT OF THE 2003 CU-HTK CONVERSATIONAL TELEPHONE…
mi.eng.cam.ac.uk/reports/svr-ftp/evermann_icassp2004.pdf27 May 2004: purpose WER. P1 supervision for VTLN 34.2P2 supervision for MLLR 28.4P3 lattice generation 24.8. ... System (P4) A B C DSAT HLDA SPron non-HLDA23.0 23.6 23.4 24.8. -
A HYBRID DISPLACEMENTESTIMATION METHOD FOR ULTRASONIC ELASTICITY…
mi.eng.cam.ac.uk/reports/svr-ftp/chen_tr615.pdf13 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. -
Applying Deep Learning in Non-native Spoken English Assessment
mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/APSIPA2019_Knill.pdf21 Feb 2022: 1.0 indicates within one CEFR grade-level. 24/45. Assessment System Performance. • ... 1.0 indicates within one CEFR grade-level. 24/45. Performance Analysis. 25/45. -
PROC IEEE, VOL. 101, NO. 5, 1160-1179, 2013 1 ...
mi.eng.cam.ac.uk/~sjy/papers/ygtw13.pdf20 Feb 2018: A similar approach is takenin [24], except that both slot values and their complements areused to build a frame. ... R(θ) = 1N. Nn=1. Tn1t=0. θ log π(ânt |bnt ,θ)Q(bnt , ânt ) (24). -
IEEE SIGNAL PROCESSING MAGAZINE [128] MAY 2010 This article ...
mi.eng.cam.ac.uk/~sjy/papers/youn10.pdf20 Feb 2018: THE HIDDEN INFORMATION STATE SYSTEMA block diagram of the HIS system is shown in Figure 13 [23], [24]. ... The history states record grounding and query status information but the details are not relevant here (see [24]). -
paper.dvi
mi.eng.cam.ac.uk/~mjfg/segdisc_2012.pdf19 Oct 2012: α̂(r) = argminα. {. F(. α, w(r), O(r); α̂(r1))}. (15). Conditional Maximum Likelihood[24]:. ... . . . . . . . vj V (24). whereV is the vocabulary of segment identities. -
A LANGUAGE SPACE REPRESENTATION FOR SPEECH RECOGNITION
mi.eng.cam.ac.uk/~mjfg/icassp15-ragni.pdf18 May 2015: There are many options to select the form of rep-resentation of the clusters and the combination method to employ[18, 17, 13, 24, 25]. ... 20, no. 6, pp. 1713–1724, 2012. [24] V. Diakoloukas and V. -
SSVM_LVCSR.dvi
mi.eng.cam.ac.uk/~mjfg/sxz_TASLP12.pdf19 Oct 2012: Flat Direct Model [11] SCRF [23] or CAug [13]. M3N [18] orLarge Margin (Multi-Class) SVM [24]. ... Reformulate equation (24) in the form of (17). 1. 2||ᾱ||22+. C. -
Towards Learning Orientated Assessment for Non-native Learner Spoken…
mi.eng.cam.ac.uk/~kmk/presentations/ALTA_Sheffield_20190306.pdf8 Mar 2019: 300 300. 25.5. 400 24.5. 400 24.4. ASR on Non-native Speech (2). • ... Thai dh d 7.24 oh aa 5.21. 30. • Top 2 recurrent substitution errors for speakers in each L1. -
On-line Active Reward Learning for Policy Optimisationin Spoken…
mi.eng.cam.ac.uk/~sjy/papers/sgmb16.pdf20 Feb 2018: Thomson and Young2010] Blaise Thomson and SteveYoung. 2010. Bayesian update of dialogue state:A pomdp framework for spoken dialogue systems.Computer Speech and Language, 24:562–588. -
main.dvi
mi.eng.cam.ac.uk/~sjy/papers/ywss05.pdf20 Feb 2018: At this point, the dialog state probabilities given by equation 24 are omputed. ... P1. find P2. (a) task 1.0. P1 b=1.0. b=0.7. b=0.3. b=0.24. -
Context adaptive training with factorized decisiontrees for HMM-based …
mi.eng.cam.ac.uk/~sjy/papers/yzmy11.pdf20 Feb 2018: 24].3 Available at http://mi.eng.cam.ac.uk/˜farm2/emphasis.4 Cohen’s Kappa cannot be used here because the phrases are not distinct elements. ... Interspeech, 2010, pp. 410–413. [24] S. Young, G. Evermann, M. Gales, T. -
Unsupervised Language Model Adaptation for Mandarin…
mi.eng.cam.ac.uk/reports/svr-ftp/mrva_icslp06.pdf20 Jan 2007: Test set baseline N-gram adaptfixed weights dynamic weights. dev05bcm (BC) 25.6 24.5eval04 (BN) 14.7 14.8dev04f (BN) 6.4 6.5. ... P3 27.4 25.6 24.5 24.3 24.3. Table 3: P2, P3 stage dev05bcm CERs. -
Towards Learning Orientated Assessment for Non-native Learner Spoken…
mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/ALTA_Sheffield_20190306.pdf21 Feb 2022: 300 300. 25.5. 400 24.5. 400 24.4. ASR on Non-native Speech (2). • ... Thai dh d 7.24 oh aa 5.21. 30. • Top 2 recurrent substitution errors for speakers in each L1. -
Reinforcement learning for parameter estimation in statistical spoken …
mi.eng.cam.ac.uk/~sjy/papers/juty12.pdf20 Feb 2018: Given the 8 subgoals (excluding the “name” subgoal) in the domain, there are 24 slot-level summary actions in total.The global summary actions implement other actions which cannot be associated ... wTα,i, wTθ,i]T C n {1,. , N}. Parameter update:24: -
4F10: Deep Learning
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect6.pdf8 Nov 2016: represents element-wise multiplication between vectors. 24/68. Long-Short Term Memory Networks (reference) [13, 10]. ... Ẽ (θ[τ]) = E (θ[τ]) νw[τ]. 50/68. Dropout [24]. Input. xd. -
JournalPaperDRAFTV0.20
mi.eng.cam.ac.uk/~sjy/papers/wipy05a.pdf20 Feb 2018: J. D. Williams, P. Poupart, S. Young 24 March 2005. University of Cambridge, Dept. ... 24. not “brittle” – i.e., they do not fail catastrophically as the actual value of errp deviates from that used in training.
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