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20 Feb 2018: Trial # users average # calls median # callsAMT 140 6.5 2Cambridge 17 24.4 20. ... vol. 24, no. 2, pp.150–174, 2010. [7] Amazon, “Amazon Mechanical Turk,” 2011.

3 Jul 2018: 24,000 fishermen a year. Mostly in storms. And not every country keeps accurate records.

13 Mar 2018: 2005. Ex-pressive speech-driven facial animation. ACM TOG 24, 4, 1283–1302. WANG, L., HAN, W., SOONG, F., AND HUO, Q.

3 Jul 2018: training set of Aurora4. Model A B C D AVGDNN/clntr 2.71 43.00 24.06 58.66 45.48VDCNN-max-4FC/clntr 2.32 35.99 21.20 ... 24, no. 12, pp. 2263-2276, Dec. 2016. Contact: j.m.rownicka@sms.ed.ac.uk.

20 Feb 2018: error rate of 33.2 %, and for the first and secondorder derivatives the error rates of the classifiers are 33.1 %and 24.2 %, respectively. ... 24.8 42.4par 22.7 13.1 21.7 32.4 32.7 25.2 27.4 45.1.

20 Feb 2018: Per-turn. WER. Per-dialog WER. None 2 6 24 83 % 0 % 0 % Low 4 12 48 83 % 32 % 28 % Med 4 12 48 77 % 46 % 41 % Hi 2 6 24 ... Dataset. Metrics (task & user sat). R2 Significant predictors. ALL User-S 52 % 1.03 Task ALL User-C 60 % 5.29 Task – 1.54

20 Feb 2018: Computer Speech and Language,24(4):562–588. O Tsimhoni, D Smith, and P Green. ... Computer Speech andLanguage, 24(2):150–174.

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.

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

20 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.

20 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

13 Mar 2018: 2005. Ex-pressive speech-driven facial animation. ACM TOG 24, 4, 1283–1302. WANG, L., HAN, W., SOONG, F., AND HUO, Q.

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%.

4 Nov 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.

20 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

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

20 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.

20 Feb 2018: Computer Speech and Language, 24(2):150–174.

20 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.

20 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.

15 Jun 2018: Sig-nificant WER improvements were observed after interpolatingwith the n-gram LM for n-best rescoring – a common practicefor speech recognition [8, 24, 25]. ... 24] S. Kombrink, T. Mikolov, M. Karafiát, and L. Burget, “Recurrentneural network

20 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

20 Feb 2018: Computer Speech and Language,24:562–588. Jason D. Williams and Steve Young. 2007.

20 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.

20 Feb 2018: POMDP-based Hidden Information State (HIS) DialogueSystem [22, 24]. ... of ICASSP, Honolulu, HI, 2007. [24] B. Thomson, J. Schatzmann, K.

20 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

20 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

20 Feb 2018: Computer Speech andLanguage, 24(4):562–588. Zhuoran Wang and Oliver Lemon. 2013. A simpleand generic belief tracking mechanism for the Dia-log State Tracking Challenge: On the believabilityof observed information.

20 Feb 2018: 24, pp. 562–588, 2010. [20] M. Lukoeviius and H. Jaeger, “Reservoir computing approachesto recurrent neural network training,” Computer Science Review,vol. ... abs/1412.2306, 2014. [24] G. Mesnil, Y. Dauphin, K. Yao, Y. Bengio, L.

20 Feb 2018: When errors are correlated belief tracking is less accurate be-cause it tends to over-estimate alternatives in the N-best list[24]. ... 24, no. 4, pp. 562–588, 2010. 17. T. Minka, “Expectation Propagation for Approximate Bayesian Inference,” in

12 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.

20 Feb 2018: F1 ICE. Slot4 95.29% 90.89% 95.72% 93.24% 0.478 89.92% 74.73% 61.56% 67.51% 0.743. ... Task - - - - - 97.12% 83.24% 64.93% 72.95% 0.175.

20 Feb 2018: 24,no. 4, pp. 562–588, Oct. 2010. [7] Jost Schatzmann, Statistical user and error modellingfor spoken dialogue systems, Ph.D.

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

13 Mar 2018: 95. Table 2: Feature definitions. Features 1-24 Differences of mean and standarddeviation features based on Yuilleand Chen box features. ... Features 24-82 Differences of mean and standarddeviation features of 18 blocks, de-noted as ‘Extended

20 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]

13 Mar 2018: Comm. ACM, 24(6):381–395, 1981. [5] G.H. Golub and C.F. Van Loan, editors. ... Int. Journal of Computer Vision, 24(3):271–300,September 1997. [16] G. Xu and Z.

13 Mar 2018: Figure 2: Resultsfrom the existing SpaceCarving algorithm using different thresholdsettings. The voxel array size was , and the thresholdswere 48,32,24,16(of 255)respectively. ... Figure 4: Resultsfrom the existing SpaceCarving algorithm using different

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

13 Mar 2018: scaling parameters, i. In particular:. 24. u1 u2 u3v1 v2 v31 1 1. ... u4v41. 35 =. 24. p11 p12 p13 p14p21 p22 p23 p24p31 p32 p33 p34.

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].

13 Mar 2018: 35 and P02 =. 24. a11 a12 a14 a13a21 a22 a24 a230 0 0 a33. ... F =. 24. 0 0 a24a330 0 a14a33. a24a11 a14a21 a24a12 a14a22 a24a13 a14a23.

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: 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: scr-10% 2.24 2.03 2.00 1.92. p <0.05, p <0.005Table 2: Human evaluation for utterance quality intwo domains.

13 Mar 2018: recognition from face motion manifolds.Image and Vision Computing, 24(5),. 2006. (in press). ... 24] R. O. Duda, P. E. Hart, and D. G. Stork.Pattern Classification.

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.

20 Feb 2018: In Proceedings of ACL, 2017. [24] Nikola Mrkšić, Diarmuid Ó Séaghdha, Blaise Thomson, Milica Gašić, Pei-Hao Su, DavidVandyke, Tsung-Hsien Wen, and Steve Young. ... Computer Speech & Language, 24(2):150–174, 2010. [53] Steve Young, Milica

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