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20 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.

20 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.

20 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.

20 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.

20 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

20 Feb 2018: Yu. 2009. The Hidden InformationState model: a practical framework for POMDPbased spoken dialogue management.ComputerSpeech and Language, 24(2):150–174.

20 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].

20 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.

20 Feb 2018: 24, no. 4, Oct. 2010. [16] S. J. Young, G. Evermann, M.

3 Jul 2018: shows that the CEDM learns to address a relationin up to 24.5% of all dialogues for r = 1.0. ... Computer Speech & Lan-guage, 24(2):150–174. Steve J. Young, Milica Gašić, Blaise Thomson, and Ja-son D.