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Real user evaluation of spoken dialogue systems using Amazon ...
mi.eng.cam.ac.uk/~sjy/papers/jkgm11.pdf20 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. -
PowerPoint プレゼンテーション
mi.eng.cam.ac.uk/UKSpeech2017/posters/e_tsunoo.pdf3 Jul 2018: 24,000 fishermen a year. Mostly in storms. And not every country keeps accurate records. -
PHONETIC AND GRAPHEMIC SYSTEMS FOR MULTI-GENRE BROADCASTTRANSCRIPTION …
mi.eng.cam.ac.uk/~ar527/wang_icassp2018.pdf4 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. -
Simplifying very deep convolutional neural network architectures for…
mi.eng.cam.ac.uk/UKSpeech2017/posters/j_rownicka.pdf3 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. -
INDICATOR VARIABLE DEPENDENT OUTPUT PROBABILITY MODELLING…
mi.eng.cam.ac.uk/~sjy/papers/tuyo01.pdf20 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. -
Active Memory Networks for Language Modeling O. Chen, A. ...
mi.eng.cam.ac.uk/~ar527/chen_is2018.pdf15 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 -
ICSLPDataCollection-10
mi.eng.cam.ac.uk/~sjy/papers/wiyo04b.pdf20 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 -
Template.dvi
mi.eng.cam.ac.uk/~ar527/chen_asru2017.pdf15 Jun 2018: LM rescoredev eval. Vit CN Vit CNng4 - 23.8 23.5 24.2 23.9. ... LM #succ words dev evalng4 23.8 24.2. uni-rnn - 21.7 22.1. -
The Effect of Cognitive Load on a Statistical Dialogue ...
mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf20 Feb 2018: Computer Speech and Language,24(4):562–588. O Tsimhoni, D Smith, and P Green. ... Computer Speech andLanguage, 24(2):150–174. -
paper.dvi
mi.eng.cam.ac.uk/~ar527/ragni_is2018a.pdf15 Jun 2018: The stage onesystem used an HTK [24] configuration that had been previ-ously employed for all Babel tasks [25, 26], multi-genre En-glish broadcast transcription [27] and many others. ... 25, no. 3, pp. 373–377, 2017. [24] S. J. Young, G. -
Emotion Conversion using F0 Segment Selection Zeynep Inanoglu, Steve…
mi.eng.cam.ac.uk/~sjy/papers/inyo08.pdf20 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. -
Use of Graphemic Lexicons for Spoken Language Assessment K.M. ...
mi.eng.cam.ac.uk/~ar527/knill_is2017.pdf15 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. -
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 -
Impact of ASR Performance on Free Speaking Language Assessment ...
mi.eng.cam.ac.uk/~ar527/knill_is2018.pdf15 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 -
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%. -
./plot_entropy.eps
mi.eng.cam.ac.uk/~ar527/chen_is2017.pdf15 Jun 2018: 24,no. 8, pp. 1438–1449, 2016. -
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. -
DISTRIBUTED DIALOGUE POLICIES FOR MULTI-DOMAIN STATISTICAL…
mi.eng.cam.ac.uk/~sjy/papers/gkty15.pdf20 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 -
Dialogue manager domain adaptation using Gaussian process…
mi.eng.cam.ac.uk/~sjy/papers/gmrs17.pdf20 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 -
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 -
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. -
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]. -
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. -
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] -
An Expressive Text-Driven 3D Talking Head
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2013-SIGGRAPH-3D-expressive-head.pdf13 Mar 2018: 2005. Ex-pressive speech-driven facial animation. ACM TOG 24, 4, 1283–1302. WANG, L., HAN, W., SOONG, F., AND HUO, Q. -
Bayesian update of dialogue state: A POMDP framework for spoken…
mi.eng.cam.ac.uk/~sjy/papers/thyo10.pdf20 Feb 2018: 564 B. Thomson, S. Young / Computer Speech and Language 24 (2010) 562–588. ... B. Thomson, S. Young / Computer Speech and Language 24 (2010) 562–588 565. -
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. -
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. -
Addressing Objects and Their Relations:The Conversational Entity…
mi.eng.cam.ac.uk/~sjy/papers/ubcr18.pdf3 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. -
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. -
DISCRIMINATIVE SPOKEN LANGUAGE UNDERSTANDINGUSING WORD CONFUSION…
mi.eng.cam.ac.uk/~sjy/papers/hgtt12.pdf20 Feb 2018: 24, no. 4, Oct. 2010. [16] S. J. Young, G. Evermann, M. -
From Discontinuous To Continuous F0 Modelling In HMM-based…
mi.eng.cam.ac.uk/~sjy/papers/yuty10.pdf20 Feb 2018: The feature set includes 24 spectralcoefficients, log F0 and 5 aperiodic component features. -
A Benchmarking Environment for ReinforcementLearning Based Task…
mi.eng.cam.ac.uk/~sjy/papers/cbsm17.pdf20 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 -
yeyo06.dvi
mi.eng.cam.ac.uk/~sjy/papers/yeyo06.pdf20 Feb 2018: g(i)jq =. T. t=1. v(t)ii d. (t)jq j, q = 1, , (d 1) (24). ... Markel,”Distance measures for speech processing”,IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.ASSP-24, no.5, pp.380-391, October 1976. -
An Expressive Text-Driven 3D Talking Head
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2013-Siggraph-Expressive-3D-VTTS.pdf13 Mar 2018: 2005. Ex-pressive speech-driven facial animation. ACM TOG 24, 4, 1283–1302. WANG, L., HAN, W., SOONG, F., AND HUO, Q. -
Uncertain RanSaC Ben Tordoff and Roberto CipollaDepartment of…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2005-MVA-Tordoff.pdf13 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. -
91_20090306_170604
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2009-MVA-Mavaddat.pdf13 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 -
Photo-Realistic Expressive Text to Talking Head Synthesis Vincent…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2013-Interspeech-EVTTS.pdf13 Mar 2018: 6] Cao, Y., Tien, W., Faloutsos, P. and Pighin, F., “Expressivespeech-driven facial animation”, ACM TOG, 24(4):1283–1302,2005. -
N-BEST ERROR SIMULATION FOR TRAINING SPOKEN DIALOGUE SYSTEMS Blaise…
mi.eng.cam.ac.uk/~sjy/papers/thgt12.pdf20 Feb 2018: 24, no. 4, pp. 562–588, 2010. [3] R. Sutton and A. -
EFFECTS OF THE USER MODEL ON SIMULATION-BASEDLEARNING OF DIALOGUE ...
mi.eng.cam.ac.uk/~sjy/papers/swsy05.pdf20 Feb 2018: The COMMUNICATOR systems in contrast onlyrequest between 24% and 43% of the unknown slots in each state. -
Camera calibration from vanishing points in images of architectural…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/1999-BMVC-photobuilder-copy.pdf13 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. -
An attention based model for off-topic spontaneous spoken response ...
mi.eng.cam.ac.uk/~ar527/malinin_slate2017.pdf15 Jun 2018: Theacoustic models are trained on 108.6 hours of BULATS test data(Gujarati L1 speakers) using the HTK v3.5 toolkit [24, 25].A Kneser-Ney trigram language model is trained ... INTERSPEECH, 2015. [24] S. Young et al., The HTK book (for HTK Version 3.4.1). -
Efficiently Combining Contour and TextureCues for Object Recognition…
mi.eng.cam.ac.uk/~cipolla/publications/inproceedings/2008-BMVC-Shotton.pdf13 Mar 2018: Puzicha. Shape matching and object recognition using shape contexts. PAMI,24(24):509–522, 2002. ... PAMI, 24(5),2002. [4] P. Dollár, Z. Tu, H. Tao, and S. -
Boosted Manifold Principal Angles for Image Set-Based Recognition…
mi.eng.cam.ac.uk/~cipolla/publications/article/2007-PR-Kim.pdf13 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. -
Still Talking to Machines (Cognitively Speaking) Steve Young…
mi.eng.cam.ac.uk/~sjy/papers/youn10a.pdf20 Feb 2018: partition ex-plicitly records the fact that x = a and the existing partitionis updated to record the fact that x = ā [24]. -
Context adaptive training with factorized decisiontrees for HMM-based …
mi.eng.cam.ac.uk/~sjy/papers/yzmy11.pdf20 Feb 2018: 24].3 Available at http://mi.eng.cam.ac.uk/˜farm2/emphasis.4 Cohen’s Kappa cannot be used here because the phrases are not distinct elements. ... Interspeech, 2010, pp. 410–413. [24] S. Young, G. Evermann, M. Gales, T. -
On-line Active Reward Learning for Policy Optimisationin Spoken…
mi.eng.cam.ac.uk/~sjy/papers/sgmb16.pdf20 Feb 2018: Thomson and Young2010] Blaise Thomson and SteveYoung. 2010. Bayesian update of dialogue state:A pomdp framework for spoken dialogue systems.Computer Speech and Language, 24:562–588.
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