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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. -
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. -
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. -
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 -
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. -
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. -
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 -
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. -
POLICY OPTIMISATION OF POMDP-BASED DIALOGUE SYSTEMS WITHOUT…
mi.eng.cam.ac.uk/~sjy/papers/ghtt12.pdf20 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. -
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 -
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. -
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. -
POLICY COMMITTEE FOR ADAPTATION IN MULTI-DOMAIN SPOKEN…
mi.eng.cam.ac.uk/~sjy/papers/gmsv15.pdf20 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 -
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 -
./plot_entropy.eps
mi.eng.cam.ac.uk/~ar527/chen_is2017.pdf15 Jun 2018: 24,no. 8, pp. 1438–1449, 2016. -
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. -
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. -
Tsiakoulis_Pirros_1293
mi.eng.cam.ac.uk/~sjy/papers/tghp12.pdf20 Feb 2018: Proc. ICASSP, Taipei, Taiwan. Thomson, B. & Young, S. (2010)“Bayesian Update of Dialogue State: A POMDP framework for spoken dialogue systems.” Computer Speech and Language 24(4):562-588.
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