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20 Feb 2018: The Knowledge Engineering Review, Vol. 00:0, 1–24. c 2006, Cambridge University PressDOI: 10.1017/S000000000000000 Printed in the United Kingdom.

15 Jun 2018: In [24] phonetic pronunciation features consisting ofa set of phone-pair distances were proposed for vowels and ap-plied to read speech. ... 8, no. 4, pp. 369–394, 1994. [24] N. Minematsu, S. Asakawa, and K.

20 Feb 2018: wwpos 24.52 11.29 18.47wspos 11.33 4.91 3.31wpof s 1.13 4.82 8.82wppof s 24.27 6.49 10.54wonset 15.08 0.33 ... 47.3% 52.7%. 75.3% 24.7%. Figure 3: Categorical quality ratings for spectral conversion duration conversion HMM-based contour generation.

15 Jun 2018: 24,no. 8, pp. 1438–1449, 2016.

20 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

15 Jun 2018: Word levelconfidence scores are returned from the Kaldi [24] decoderwhich are frame weighted and undergo a piece-wise mappingfor use in error detection. ... 3660–3664. [24] D. Povey et al., “The Kaldi Speech Recognition Toolkit,” in Proc.of the

20 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%.

20 Feb 2018: 24, no. 4, pp. 562–588, 2010. [13] M Gašić, C Breslin, M Henderson, D Kim, M Szummer,B Thomson, P Tsiakoulis, and S Young, “POMDP-based dia-logue manager adaptation

20 Feb 2018: SFRName Reward Success #Turnsbest prior 8.66 0.35 85.40 2.19 8.32 0.20adapted 9.62 0.30 89.60 1.90 8.24 0.19. ... The systemwas deployed in a telephone-based set-up, with subjects recruited via Ama-zon MTurk and a recurrent neural network model was used

20 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