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paper.dvi
mi.eng.cam.ac.uk/~mjfg/liao_ICASSP07.pdf15 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. -
14 INGENIA INGENIA ISSUE 54 MARCH 2013 15 Talking ...
mi.eng.cam.ac.uk/~sjy/papers/youn13.pdf20 Feb 2018: Typical word error rates are now between 3 and 8% for clean, carefully spoken speech, rising to 20% or greater for conversational speech in noisy environments. ... XXXXXXTALKING TO MACHINES. INGENIA ISSUE 54 MARCH 2013 21 20 INGENIA. -
Uncertainty management for on-line optimisation of a…
mi.eng.cam.ac.uk/~sjy/papers/dgcg11.pdf20 Feb 2018: A positive reward of 20 isgiven at the end of the dialogue if the DM managed to fulfilthe user request and a penalty of 1 is applied per system turnto encourage ... The new exploration schemes were evaluated in this paperusing GP-SARSA. In the future, -
Real user evaluation of spoken dialogue systems using Amazon ...
mi.eng.cam.ac.uk/~sjy/papers/jkgm11.pdf20 Feb 2018: complete at least 20 dialoguesduring the session, under continuous supervision of a researchteam member. ... Trial # users average # calls median # callsAMT 140 6.5 2Cambridge 17 24.4 20. -
naacl.dvi
mi.eng.cam.ac.uk/~sjy/papers/stwy07.pdf20 Feb 2018: Usergoals are randomly generated and an (arbitrary) rewardfunction assigning 20 points for successful completionand -1 for every dialogue turn is used. -
Natural Belief-Critic: a reinforcement algorithm for parameter…
mi.eng.cam.ac.uk/~sjy/papers/jtkm10.pdf20 Feb 2018: The final system was evaluated as in the firsttask; although in this case, the training and evaluation procedurewas executed 20 times. ... by 10%-20% in comparison with the rewards obtained whenusing the handcrafted parameters. -
A NORMALIZATION METHODFOR AXIAL-SHEAR STRAIN ELASTOGRAPHY L. Chen, R. …
mi.eng.cam.ac.uk/reports/svr-ftp/chen_tr645.pdf13 May 2010: effect very similar to that observed with non-axially aligned,elliptical inclusions [20, 21]. ... This is very likely the “fillin” effect [20, 21]. 6. (a) 10 mm 0. -
Evaluation of Statistical POMDP-basedDialogue Systems in Noisy…
mi.eng.cam.ac.uk/~sjy/papers/ybgh14.pdf20 Feb 2018: This isdone by letting the dialogue system interact with a user simulator[20]. ... vol. 7, no. 3, 2011. 20. J. Schatzmann, B. Thomson, K. Weilhammer, H. -
SPOKEN LANGUAGE UNDERSTANDING FROM UNALIGNED DATA USINGDISCRIMINATIVE …
mi.eng.cam.ac.uk/~sjy/papers/mgjk09.pdf20 Feb 2018: by specifying a VoiceXML grammarthat instantiates slot values. Within the past 20 years, research on spoken language under-standing (SLU) has produced models that alleviate these issuesby learning to derive a ... 94.20 95.26TownInfo dataset with ASR -
DISCRIMINATIVE SPOKEN LANGUAGE UNDERSTANDINGUSING WORD CONFUSION…
mi.eng.cam.ac.uk/~sjy/papers/hgtt12.pdf20 Feb 2018: F-score. C.Net. ContextPhoenix. Predicted F-score. 0 20 40 60 80 100. ... 20,no. 4, pp. 495–514, Oct. 2006. [20] M. Gašić, P. Tsiakoulis, M. -
The Development of the Cambridge UniversityRT-04 Diarisation System…
mi.eng.cam.ac.uk/research/projects/EARS/pubs/tranter_rt04.pdf15 Feb 2005: sorting didev03 eval03 sttdev04 dev04f2 devallnone 18.0 15.9 21.2 26.9 20.3time 17.5 16.7 21.5 25.7 20.2spkr-start 17.5 17.9 ... 18.5. 19. 19.5. 20. mea. n D. ER. on. all. 24 d. -
POLICY OPTIMISATION OF POMDP-BASED DIALOGUE SYSTEMS WITHOUT…
mi.eng.cam.ac.uk/~sjy/papers/ghtt12.pdf20 Feb 2018: It can be shown [20] that the marginal likelihood of the observedrewards is modelled by. ... 20] M Gašić, Statistical Dialogue Modelling, PhD thesis, Univer-sity of Cambridge, 2011. -
sigdial11_sdc10-Feb27-V2
mi.eng.cam.ac.uk/~sjy/papers/bbch11.pdf20 Feb 2018: Table 4: Average dialogue WER. 0 20 40 60 80 100. ... WER for the three fully tested systems. 0 20 40 60 80 100. -
lect1.dvi
mi.eng.cam.ac.uk/~mjfg/local/4F10/lect1_pres.pdf10 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. -
SYSTEM COMBINATION AND SCORE NORMALIZATION FOR SPOKEN TERM DETECTION
mi.eng.cam.ac.uk/~mjfg/ICASSP13_ibm1.pdf13 Jun 2013: First, we show score normaliza-tion methodology that improves in average by 20% keyword searchperformance. ... 20–27. [20] X. Cui, J. Xue, X. Chen, P. A. Olsen, P. -
JOINT MODELLING OF VOICING LABEL AND CONTINUOUS F0 FOR ...
mi.eng.cam.ac.uk/~sjy/papers/yuyo11a.pdf20 Feb 2018: Toreduce the noise introduced by forced choices, the 10 wave file pairswere duplicated and the order of the two systems were swapped.The final 20 wave file pairs were then shuffled -
THE USE OF DISCRIMINATIVE BELIEF TRACKINGIN POMDP-BASED DIALOGUE…
mi.eng.cam.ac.uk/~sjy/papers/khgt14.pdf20 Feb 2018: 0 5000 10000 15000 20000 25000 30000Training dialogues. 20. 15. 10. ... 20. 15. 10. 5. 0. 5. Ave. rage. rew. ard. RNNBNBN (constrained). -
Addressing Objects and Their Relations:The Conversational Entity…
mi.eng.cam.ac.uk/~sjy/papers/ubcr18.pdf3 Jul 2018: 1 with rew =23.4,suc = 99.7% and in Env. 3 with rew =20.1,suc = 94.5%. ... 2016. Towards anontology-driven adaptive dialogue framework. InProceedings of the 1st International Workshop onMultimedia Analysis and Retrieval for MultimodalInteraction, pages -
Use of Deep Learning in Free Speaking Non-native English Assessment
mi.eng.cam.ac.uk/~mjfg/ALTA/presentations/TSD2021_Knill.pdf21 Feb 2022: dependent. • General approach tunable approach based on deep learning. 20/56. Model-based Pronunciation Features. ... General approach tunable approach based on deep learning. 20/56. Deep Learning Pronunciation Features [5]. -
Woodland et al.: English CTS Systems 2003 CU-HTK English ...
mi.eng.cam.ac.uk/research/projects/EARS/pubs/woodland_rt03s.pdf24 Jul 2003: 7HLDA pruned bg 20.0 34.4 34.0 29.4MPElattice regen/comb 19.4 34.0 33.6 28.9. % ... Cambridge UniversityEngineering Department. Rich Transcription Workshop 2003 20. Woodland et al.: English CTS Systems.
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