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POMDP-based dialogue manager adaptation to extended domains M.…
mi.eng.cam.ac.uk/~sjy/papers/gbhk13a.pdf20 Feb 2018: Computer Speech and Language,24(4):562–588. B Thomson, M Gašić, M Henderson, P Tsiakoulis, andS Young. ... Computer Speech and Language, 24(2):150–174. B Zhang, Q Cai, J Mao, E Chang, and B Guo.2001. -
Multi-domain Neural Network Language Generation forSpoken Dialogue…
mi.eng.cam.ac.uk/~sjy/papers/wgmr16.pdf20 Feb 2018: scr-10% 2.24 2.03 2.00 1.92. p <0.05, p <0.005Table 2: Human evaluation for utterance quality intwo domains. -
The Hidden Information State model: A practical framework for…
mi.eng.cam.ac.uk/~sjy/papers/ygkm10.pdf20 Feb 2018: S. Young et al. / Computer Speech and Language 24 (2010) 150–174 151. ... 152 S. Young et al. / Computer Speech and Language 24 (2010) 150–174. -
IEEE TRANS. ON ASLP, TO APPEAR, 2011 1 Continuous ...
mi.eng.cam.ac.uk/~sjy/papers/yuyo11.pdf20 Feb 2018: This mixed excitation model hasbeen shown to give significant improvements in the quality ofthe synthesized speech [24]. ... 63.5% 36.5%Male. CF-HMM. 75.5% 24.5%. 0% 25% 50% 75% 100%. Female. -
main.dvi
mi.eng.cam.ac.uk/~sjy/papers/youn0720 Feb 2018: Hence, an itera-tive algorithm can be implemented which repeatedlyscans through the vocabulary, testing each word tosee if moving it to some other class would increasethe likelihood [24]. ... Thed p nuisance dimensions are modelled by a -
Optimisation for POMDP-based Spoken Dialogue Systems M. Gašić, F.…
mi.eng.cam.ac.uk/~sjy/papers/gjty12.pdf20 Feb 2018: To obtain a closed formsolution of (24), the policy π must be differentiable with respect to θ. ... 10. To lower the variance of the estimate of the gradient, a constant baseline, B, can beintroduced into (24) without introducing any bias [22]. -
crosseval_diff-reward2b.ps
mi.eng.cam.ac.uk/~sjy/papers/kgjm10.pdf20 Feb 2018: Yu. 2009. The Hidden InformationState model: a practical framework for POMDPbased spoken dialogue management.ComputerSpeech and Language, 24(2):150–174. -
Phrase-based Statistical Language Generation usingGraphical Models…
mi.eng.cam.ac.uk/~sjy/papers/mgjk10.pdf20 Feb 2018: Com-puter Speech & Language, 24(4):562–588, 2010. Y. Tokuda, T. Yoshimura, T. ... Computer Speech and Language,24(2):150–174, 2010. -
IEEE TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, JANUARY…
mi.eng.cam.ac.uk/~sjy/papers/gayo14.pdf20 Feb 2018: An advantage of this sparsification approach is that itenables non-positive definite kernel functions to be used in theapproximation, for example see [24]. ... It has already beenshown that active learning has the potential to lead to fasterlearning [24] -
PHONETIC AND GRAPHEMIC SYSTEMS FOR MULTI-GENRE BROADCASTTRANSCRIPTION …
mi.eng.cam.ac.uk/~mjfg/ALTA/publications/ICASSP2018_YuWang.pdf12 Sep 2018: 23] L. Breiman. Bagging predictors. Machine learning,24(2):123–140, 1996. [24] O. Siohan, B. ... IEEE/ACM Transactions on Audio, Speech,and Language Processing, 24(8):1438–1449, 2016. Introduction. Graphemic English systems.
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