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ERROR SIMULATION FOR TRAINING STATISTICAL DIALOGUE SYSTEMS Jost…
mi.eng.cam.ac.uk/~sjy/papers/scty07b20 Feb 2018: POMDP-based Hidden Information State (HIS) DialogueSystem [22, 24]. ... of ICASSP, Honolulu, HI, 2007. [24] B. Thomson, J. Schatzmann, K. -
ON-LINE POLICY OPTIMISATION OF BAYESIAN SPOKEN DIALOGUE SYSTEMS…
mi.eng.cam.ac.uk/~sjy/papers/gbhk13.pdf20 Feb 2018: 3.3. The agenda-based simulated user. The agenda-based user simulator [24, 25] factorises the user stateinto an agenda and a goal. ... 23] TopTable, “TopTable,” 2012, https://www.toptable.com. [24] J Schatzmann, Statistical User and Error Modelling -
BAYESIAN DIALOGUE SYSTEM FOR THE LET’S GO SPOKEN DIALOGUE ...
mi.eng.cam.ac.uk/~sjy/papers/tykg10.pdf20 Feb 2018: In control tests by humanusers, the success rate of the system was 24.5% higher thanthe baseline Lets Go! ... Com-pared to the BASELINE system, the BUDSLETSGO systemimproves the dialogue success rate by 24.5% and the worderror rate by 9.7%. -
The Knowledge Engineering Review, Vol. 00:0, 1–24. c© 2006, ...
mi.eng.cam.ac.uk/~sjy/papers/swsy06.pdf20 Feb 2018: The Knowledge Engineering Review, Vol. 00:0, 1–24. c 2006, Cambridge University PressDOI: 10.1017/S000000000000000 Printed in the United Kingdom. -
Towards Using Conversations with Spoken Dialogue Systems in…
mi.eng.cam.ac.uk/~sjy/papers/lygk16.pdf20 Feb 2018: Corpus Mean (SD) Grades Correlationn Human Auto R p. L 21 24.2 (3.1) 17.1 (1.9). ... 69C 50 24.0 (3.0) 15.6 (3.3). 59. 01. Table 2: Mean (standard deviation) of human andautomated grades, along with Pearson’s correla-tions between the human and -
PROBABLISTIC MODELLING OF F0 IN UNVOICED REGIONS IN HMM ...
mi.eng.cam.ac.uk/~sjy/papers/ytgk09.pdf20 Feb 2018: The static feature set comprised 24 Mel-Cepstral coefficients,logarithm of F0 and aperiodic energy components in five frequency. ... 12 sentences werethen randomly selected to make up a testset for each listener, leadingto 24 wave files pairs (12 for -
POLICY COMMITTEE FOR ADAPTATION IN MULTI-DOMAIN SPOKEN…
mi.eng.cam.ac.uk/~sjy/papers/gmsv15.pdf20 Feb 2018: 24, no. 2, pp. 395–429, Apr. 2010. [8] Pierre Lison, “Multi-policy dialogue management,” inProceedings of the SIGDIAL 2011 Conference, Strouds-burg, PA, USA, 2011, SIGDIAL ’11, pp. ... 24, no. 4,pp. 562–588, 2010. [18] T Jebara, R Kondor, and A -
POLICY OPTIMISATION OF POMDP-BASED DIALOGUE SYSTEMS WITHOUT…
mi.eng.cam.ac.uk/~sjy/papers/ghtt12.pdf20 Feb 2018: 1.2% 2.0%Request 17.4% 24.5% 18.4% 24.4%. ... 24, no. 4, pp. 562–588, 2010. [22] J Peters and S Schaal, “Natural Actor-Critic,” Neurocomput-ing, vol. -
Mark John Francis Gales Thesis, Book Chapters and Magazines ...
mi.eng.cam.ac.uk/~mjfg/publications_list.pdf11 Oct 2023: Audio Speech Lang.Process., vol. 24, no. 8, pp. 1438–1449, 2016. 9. ... Signal Processing,ICASSP 2015, South Brisbane, Queensland, Australia, April 19-24, 2015, pp. -
Cross-Lingual Spoken Language Understanding from Unaligned Data…
mi.eng.cam.ac.uk/~sjy/papers/lemy10.pdf20 Feb 2018: on Mancorpora)a 91.40 90.17 90.24. 90.20Auto (Google MT) 90.81 90.77 87.72 89.223. ... 24, no. 2, pp. 150–174, April 2010. [8] P. Koehn, H. -
Inverse Reinforcement Learning for Micro-Turn Management Dongho Kim,…
mi.eng.cam.ac.uk/~sjy/papers/kgbt14.pdf20 Feb 2018: 0.24. 0.22. 0.20. 0.18. 0.16. Log-. likel. ihoo. dpe. rmic. ro-tu. ... 24, no. 4, pp. 562–588, 2010. [14] SpaceBook. EC FP7/2011-16, grant number 270019. -
acl2010.dvi
mi.eng.cam.ac.uk/~sjy/papers/gjkm10.pdf20 Feb 2018: Computer Speech and Language, 24(2):150–174. -
tech.dvi
mi.eng.cam.ac.uk/~sjy/papers/bghk13.pdf20 Feb 2018: 24, pp. 562–588, 2010. [3] G. Aist, J. Allen, E. Campana, C. ... 24] M. Henderson, M. Gašić, B. Thomson, P. Tsiakoulis, K.Yu,and S. -
sigdial11_sdc10-Feb27-V2
mi.eng.cam.ac.uk/~sjy/papers/bbch11.pdf20 Feb 2018: 6% 24.6% 14.7% 9.6%. ... Length (s) Turns/call Words/turn. SYS1 control 155 18.29 2.87 (2.84) SYS1 live 111 16.24 2.15 (1.03) SYS2 control 147 17.57 1.63 -
Incremental on-line adaptation of POMDP-based dialogue managers…
mi.eng.cam.ac.uk/~sjy/papers/gktb14.pdf20 Feb 2018: 24, no. 2, pp.150–174, 2010. [5] B. Thomson and S. Young, “Bayesian update of dialogue state:A POMDP framework for spoken dialogue systems,” ComputerSpeech and Language, vol. ... 24, no. 4, pp. 562–588, 2010. [6] M. Gašić, C. Breslin, M. -
1 Structured Log Linear Models for Noise RobustSpeech Recognition ...
mi.eng.cam.ac.uk/~mjfg/zhang10.pdf8 Sep 2010: Speech Lang.,vol. 24, no. 4, pp. 648–662, 2010. [8] B. Taskar, “Learning structured prediction models: a large marginapproach,” Ph.D. -
Reward Shaping with Recurrent Neural Networks for Speeding upOn-Line…
mi.eng.cam.ac.uk/~sjy/papers/svgm15.pdf20 Feb 2018: Computer Speech and Language,24:562–588. Jason D. Williams and Steve Young. 2007. -
Dialogue Context Sensitive Speech Synthesis using Factorized Decision …
mi.eng.cam.ac.uk/~sjy/papers/tgbh14.pdf20 Feb 2018: In mostcases, a data-driven approach is followed, either by detect-ing/annotating emphasized words in existing corpora [23, 10] orby collecting speech corpora specifically designed for emphasismodeling [24]. ... Appointment Booking Task -
Uncertainty management for on-line optimisation of a…
mi.eng.cam.ac.uk/~sjy/papers/dgcg11.pdf20 Feb 2018: 24, no. 2, pp. 150–174,2010. [6] W. Eckert, E. Levin, and R. ... 9] O. Pietquin, M. Geist, S. Chandramohan, and H. Frezza-Buet,“Sample-Efficient Batch Reinforcement Learning for DialogueManagement Optimization,” ACM Transactions on Speech -
Combining I-vector Representation and Structured Neural Networks for…
mi.eng.cam.ac.uk/~mjfg/icassp16_wu.pdf5 Apr 2016: Speech andSignal Processing, ICASSP 2015, South Brisbane, Queens-land, Australia, April 19-24, 2015, 2015, pp. ... IEEE, 2015, pp. 4315–4319. [24] Mark JF Gales, “Cluster adaptive training of hidden markovmodels,” Speech and Audio Processing, IEEE
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