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  1. Results that match 1 of 2 words

  2. Segment Generation and Clustering in the HTK Broadcast News…

    mi.eng.cam.ac.uk/reports/full_html/hain_darpa98.html/
    1 Mar 2000: S. 0.56. 98.24. 1.20. T. 0.00. 1.19. 98.81. a). M. S.
  3. Abstract for woodland_lvcsr01

    mi.eng.cam.ac.uk/reports/abstracts/speech/woodland_lvcsr01.html
    27 Jul 2020: In the March 2001 Hub5 evaluation the CU-HTK system achieved an overall word error rate of 24.6%, which was the lowest word error rate by a statistically significant margin. |
  4. A Method for Direct Audio Search with Applications to Indexing and…

    mi.eng.cam.ac.uk/reports/full_html/johnson_icassp00.html/
    16 Jun 2000: Of the 110 seconds worth of news'' rejected by the system with shorter windows, 24 seconds was due to an interview with Bill Clinton being re-broadcast, and the rest was
  5. Shape Context and Chamfer Matching in Cluttered Scenes A. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/thayananthan_cvpr03.pdf
    12 May 2003: Belongie,J. Malik, and J. Puzicha. Shapematchingandobjectrecognitionusingshapecontexts. IEEE Trans.PatternAnalysisand Machine Intell., 24(4):509–522,April 2002.
  6. The CUHTK-Entropic 10xRT Broadcast News Transcription System

    mi.eng.cam.ac.uk/reports/full_html/odell_darpa99.html/
    1 Mar 2000: Comparing Table 4 with Table 3 the advantage of including two passes can clearly be seen: on BNeval98 the error rate was reduced by 24% while the overall computation was increased
  7. Speaker Diarisation for Broadcast News

    mi.eng.cam.ac.uk/reports/full_html/tranter_odyssey04.html/
    14 Jun 2004: 9.1. 25.54. 29.7. 46.14. CU. MIT. 2.1. 2.5. 5.3. 24.23. 24.7. ... Adv. Seg. Clust. Score. Metric. NONE. CUED. MIT. 24.23. Primary. CUED_TDT4.
  8. Spoken Document Retrieval for TREC-8 at Cambridge University

    mi.eng.cam.ac.uk/reports/full_html/johnson_trec8.html/
    30 Mar 2000: 24.7. 35.5. 10181. Windowing - 30s@15s. 33.9. 46.1. 18669. Windowing - 30s@10s. ... 55.73. 52.75. 43.89. ----. Sheffield. 32.0. 44.7. 60.66. 52.85. 42.47. 38.24.
  9. tr.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/prager_tr436.pdf
    28 Jun 2002: Communicationsof the ACM, 24(6):381–395, June 1981. [4] Jr. J. M. Kofler and E. ... Ultrasound in Medicine and Biology,24(4):535–542, 1998. [7] J. J. More. The Levenberg-Marquardt algorithm: implementation and theory.
  10. Class-based language model adaptation using mixtures of word-class…

    mi.eng.cam.ac.uk/reports/full_html/moore_icslp00.html/
    2 Nov 2000: 225.85. 192.66. 224.24. 187.37. 1003. 250. 158.98. 151.15. 158.68. 149.37.
  11. Spoken Document Retrieval for TREC-9 at Cambridge University

    mi.eng.cam.ac.uk/reports/full_html/johnson_trec9.html/
    23 Feb 2002: 183k articles in [24]), in previous work we found that increasing the parallel corpus size to approximately 110k articles did not help performance [16]. ... 45.02. cr-limsi2. 21.2. 37.24. 39.28. 41.62. 44.12. cr-limsi1. 21.5. 36.56. 38.57.
  12. Stradwin files

    mi.eng.cam.ac.uk/~gmt11/stradwin/stradwin_files.htm
    RES_VID_GREY_THRESH. long. 24. IMAGE. When recording Doppler data, the intensity threshold (as an 8-bit integer) below which a colour video signal is regarded as being grey-scale.
  13. narrow wheels (8) do ub le p ins ( ...

    mi.eng.cam.ac.uk/IALego/trays.pdf
    1 Jan 2024: axle. s (. 80). bush pins (16). plates (46). 127 beams (24)o.
  14. Clinically Practical Freehand Three-Dimensional Ultrasound

    mi.eng.cam.ac.uk/research/projects/cp3dus/
    22 Jul 2010: Achievements. Freehand RF 3D ultrasound acquisition. We have successfully developed the real-time, RF 3D ultrasound acquisition system [24]. ... 24] G. Treece, R. Prager and A. Gee. Freely available software for 3D RF ultrasound.
  15. Woodland et al.: English CTS Systems 2003 CU-HTK English ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/woodland_rt03s.pdf
    24 Jul 2003: CNC P4.[123]P5.[123] 19.8 24.3 27.0 23.9%WER on eval02 for all stages of 2002 system, manual segmentation. • ... Final NCE is 0.318. Cambridge UniversityEngineering Department. Rich Transcription Workshop 2003 24.
  16. ICASSP2014b.dvi

    mi.eng.cam.ac.uk/~mjfg/yoshioka_ICASSP14.pdf
    3 Apr 2014: Workshop. Automat. SpeechRecognition, Understanding, 2011, pp. 24–29. [13] O. Abdel-Hamid and H. ... Mag.,vol. 29, no. 6, pp. 114–126, 2012. [24] T. Yoshioka, X.
  17. Audio Indexing and Retrieval of Complete Broadcast News Shows

    mi.eng.cam.ac.uk/reports/full_html/johnson_riao00.html/
    19 Apr 2000: 39.39. 27.42. 752913. -. -. N. B. 95.8. 37.52. 24.92. 698461. -. -. ... In Proc. TREC-7, NIST SP 500-242, pages 1-24, Gaithersburg, MD.
  18. 3 Nov 2023: 24] Semi-supervised pre-training and back-translation fine-tuning for translation of formal and informal text.
  19. LARGE SCALE MMIE TRAINING FOR CONVERSATIONAL TELEPHONE SPEECH…

    mi.eng.cam.ac.uk/reports/full_html/woodland_stw00.html/
    5 Oct 2000: The bigram 1-best hypotheses had a 24.6% word error rate (WER) and a Lattice WER (LWER) of 6.2%...
  20. CU-HTK March 2001 Hub5 system Phil Woodland, Thomas Hain, ...

    mi.eng.cam.ac.uk/reports/svr-ftp/woodland_lvcsr01.pdf
    31 May 2001: Cambridge UniversityEngineering Department. Hub5 Workshop 24. Woodland, Hain, Evermann & Povey: CU-HTK March 2001 Hub5 system. ... 5 29.2 24.6Rover vote 20.1 25.2 30.2 25.3Rover conf 19.9 24.6 29.8 24.9.
  21. 16 Jun 2008: 20 I. 2. –24. yst1yst. yst1. 35 = Dyet (13). where D is the dynamic coefficient matrix and yet is the vectorof static coefficients in the appropriate window.
  22. The Cambridge University March 2005 Speaker Diarisation System

    mi.eng.cam.ac.uk/reports/full_html/sinha_eurospeech05.html/
    22 Sep 2005: The experiments reported in this paper use a development set of 24 US broadcast news shows, denoted.
  23. LARGE SCALE DISCRIMINATIVE TRAINING FORSPEECH RECOGNITION P.C.…

    mi.eng.cam.ac.uk/reports/svr-ftp/woodland_asr00.pdf
    6 Nov 2000: Some discriminative training schemes, such as frame-discrimination [14, 24], try to over-generate training set con-fusions to improve generalisation. ... In [24] it was shownthat the improvements obtained by FD were at least as goodas those reported by
  24. Uncertainty: Knowing What You Don't Know

    mi.eng.cam.ac.uk/~mjfg/mjfg_cncc2021.pdf
    20 Jun 2023: 24/36. Spoken Language Assessment. 25/36. Grader Uncertainty: Ensemble-Based. Within 0.5 CEFR-Level Within 1.0 CEFR-Level26/36.
  25. MultiMedia Document Retrieval (1997-2000) - Progress

    mi.eng.cam.ac.uk/research/projects/Multimedia_Document_Retrieval/progress.html
    7 Oct 2001: The overall word error rate for the data was 24.8%, the lowest in the TREC-7 SDR evaluation, with the baselines provided by NIST offering 33 and 42%. ... The final project publications include the TREC-9 paper [24] , two papers in the International
  26. Unsupervised Bayesian Detection of Independent Motion in Crowds…

    mi.eng.cam.ac.uk/reports/svr-ftp/brostow_MotionInCrowdsCVPR06.pdf
    14 Sep 2006: 24, 18]. Both systems group an image’sspatial features, performing a global annealing optimizationthat propagates the certainty at distinct person-boundaries touncertain areas where those people’s outlines are ambigu-ous. ... 24] P. Tu and J.
  27. 9 Aug 2005: #"$%&')(,-.%%&( 0/-#1""324%5/246"78&9:5. ;<8<'7>=@?9A@BA@BDCE<8F@<'8=HGIBDJK=B&L>MNMNO>PQ'/I JRI =BSA@BDCT8=G>=UVI ONJW(XY=ZL@Q. "M>L[AXYU]M>BDU=>_8B?IBDMNM>XaIBD?HQ.BI bDM>XcJ4I Ued=>_#'A@]G[XaI C?@M. ;'&'f2#PI JL[A@L[M>XCMNJKO>XgI,G[MNJABDMNhA@i? @
  28. paper.dvi

    mi.eng.cam.ac.uk/~mjfg/ragni_ASRU11.pdf
    20 Dec 2011: 24, pp. 648–662, 2010. [5] S.-X. Zhang, A. Ragni, and M.
  29. 19 Dec 2006: Uncertainty 1 4 16 256. Clean — 33.2. SPLICENo. 24.6 20.7 17.0 12.3FE-CMLLR 16.3 15.3 12.8 13.5.
  30. MORTGAGE DEFAULT: CLASSIFICATION TREES ANALYSIS David Feldman* and…

    mi.eng.cam.ac.uk/~mjfg/local/4F10/Feldman_Gross.pdf
    15 Nov 2005: MORTGAGE DEFAULT: CLASSIFICATION TREES ANALYSIS. David Feldman and Shulamith Gross#. March 24, 2003. ... 24. robust Cross-Validation method. Since it does not have any particular.
  31. 20 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.
  32. TWO-WAY CLUSTER VOTING TO IMPROVE SPEAKER DIARISATION PERFORMANCE S.…

    mi.eng.cam.ac.uk/reports/svr-ftp/tranter_icassp05.pdf
    25 Mar 2005: shows. The DER is 24.16%(28.05%) if the best(worst) input istaken independently for each show. ... true reference speakers, so when scoringagainst the reference, the supergroups may no longer be treated indepen-dently thus dramatically increasing the
  33. INVESTIGATION OF ACOUSTIC MODELING TECHNIQUES FOR LVCSR SYSTEMS X. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/liu_icassp2005.pdf
    19 May 2005: Systemeval03. dev04s25 fsh Avg. P2-cn HLDA 26.6 18.4 22.6 18.7. P3a-cn SAT 24.5 17.1 20.9 17.3P3c-cn SPron 24.7 ... cn SPAM 24.1 16.9 20.6 17.2P3h-cn SATSPAM 23.9 16.9 20.5 16.8P3i-cn CTRL 24.5 17.5 21.1 17.6.
  34. 9 Nov 2023: Talk. [24] Context-dependent alignment models and hierarchical phrase-based translation with weighted finite state trans-ducers, GALE PI Meeting, Tampa, FL, USA, May 2009. ... Association for Computational Linguistics, November 2021. [24] BO-HSIANG TSENG,
  35. 20 Feb 2018: partition ex-plicitly records the fact that x = a and the existing partitionis updated to record the fact that x = ā [24].
  36. 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.
  37. Part Name Part Image Part Weight [mg] Electric, Motor ...

    mi.eng.cam.ac.uk/IALego/helicopter_files/helicopter_weights.pdf
    1 Jan 2024: 1300. Light Bluish Gray Technic, Gear 24 Tooth (New Style. with Single Axle Hole).
  38. The Cambridge Multimedia Document Retrieval (MDR) Project : Summary…

    mi.eng.cam.ac.uk/reports/full_html/sparckjones_cltr517.html/
    10 Oct 2001: X) 64.95 61.25 46.82 45.24 44.27 30.33 39.98 42.14 38.65 35.72 BE DBRF2 (X) 66.57 65.89 45.21 ... BE DBRF (X) 69.41 69.12 52.55 51.73 51.14 36.86 41.99 44.13 42.14 42.05 BE [PR]BRF (A) 66.24 64.53
  39. Deep Learning for Speech Recognition

    mi.eng.cam.ac.uk/~mjfg/LxMLS17.pdf
    29 Nov 2017: Network Interpretation [24]. Standard /ay/ Stimulated /ay/. • Deep learning usually highly distributed - hard to interpret• awkward to adapt/understand/regularise• modify training - add stimulation regularisation• improves ASR performance.
  40. 7 Oct 2001: 8;,=&"vZ$9V K)i!Zi9V ;)Î$9h ;& ;=!"p;) / -,H;&AP,!#P6 %S+[#( 24 )g(>@{7cPU(.( 7;'8;(! ... 9V @24 Ì ' $;S+[ -c.,=",=&8U - PU -"P7,=!)G,=&,=&,H&8g- / -( -,=,Hg",=-;' - -r( -,!K9$(-v> zXx}24 & -)g=![] Ç4',!
  41. ICSLPDataCollection-10

    mi.eng.cam.ac.uk/~sjy/papers/wiyo04b.pdf
    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
  42. The Effect of Cognitive Load on a Statistical Dialogue ...

    mi.eng.cam.ac.uk/~sjy/papers/gtht12.pdf
    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.
  43. 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.
  44. 20 Feb 2018: POMDP-based Hidden Information State (HIS) DialogueSystem [22, 24]. ... of ICASSP, Honolulu, HI, 2007. [24] B. Thomson, J. Schatzmann, K.
  45. 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.
  46. 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
  47. Mark John Francis Gales Thesis, Book Chapters and Magazines ...

    mi.eng.cam.ac.uk/~mjfg/publications_list.pdf
    11 Oct 2023: Audio Speech Lang.Process., vol. 24, no. 8, pp. 1438–1449, 2016. 9. ... Signal Processing,ICASSP 2015, South Brisbane, Queensland, Australia, April 19-24, 2015, pp.
  48. 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%.
  49. 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
  50. POLICY COMMITTEE FOR ADAPTATION IN MULTI-DOMAIN SPOKEN…

    mi.eng.cam.ac.uk/~sjy/papers/gmsv15.pdf
    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
  51. 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

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