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  1. Results that match 2 of 3 words

  2. Factor analysed hidden Markov models for speech recognition A-V.I. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/rosti_csl04.pdf
    22 May 2004: Factor analysed hidden Markov models for. speech recognition. A-V.I. Rosti, M.J.F. GalesCambridge University Engineering Department, Trumpington Street, Cambridge,. CB2 1PZ, UK. Abstract. Recently various techniques to improve the correlation model
  3. RAO-BLACKWELLISED GIBBS SAMPLING FOR SWITCHING LINEAR…

    mi.eng.cam.ac.uk/reports/svr-ftp/rosti_icassp2004.pdf
    22 May 2004: Togive an idea of the range of these N -best lists the oracle (best) er-ror rate on the tst data was 1.24% and the “idiot” (worst) errorrate was
  4. Diarisation Research at CUED Sue Tranter and Srinivasan Umesh ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/tranter_mdetechmay04.pdf
    29 Apr 2004: 29.44 0 6standard-BIC-judge, full cov 24.62 2 2standard-BIC-judge, diag cov 23.90 3 1standard-BIC-judge, 128mix GMM 23.76 5 1IDIAP-BIC-judge, full ... 37.92 10.74 24.62BIC-judge, diag cov 30.30 19.94 18.11 31.05 37.92 10.74 23.90BIC-judge, 128mix GMM
  5. 26 Jan 2004: The likelihood of an observation given thestate is simply. p(oj|x) = N(oj; Cx µ(o), Σ(o)) (24).
  6. Progress in Broadcast News English Transcription D.Y. Kim, M.J.F. ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/kim_sttmay04.pdf
    25 May 2004: 16 20 24 28. dev03 bnac 144 17.9bnacTDT4 375 16.8bnacTDT4TDT2 749 16.8 16.5 16.4bnacTDT4TDT2TDT4a 1126 16.7 16.3 16.0 16.1bnacTDT4TDT4a 752 16.7
  7. Linear Gaussian Models for Speech Recognition

    mi.eng.cam.ac.uk/reports/svr-ftp/rosti_thesis.pdf
    8 Oct 2004: ix. 2.7 Summary 23. 3 Generalised Linear Gaussian Models 24. 3.1 State Space Models 24. ... bank of triangular filters (eg. 24 channels). This smoothed power spectrum is compressed using.
  8. Cambridge STT Overview P.C. Woodland, H.Y. Chan, G. Evermann, ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/woodland_earsfeb04.pdf
    23 Mar 2004: Eval03 with CU-HTK P1-P2 System. Overall Swbd Fisher Male Female. h5train03b LM03 24.6 28.7 20.2 25.7 23.5h5train03b LM03Fsh3896 23.9 28.2 19.3 ... 25.0 22.8fisher3896 LM03Fsh3896 23.1 27.0 18.9 24.6 21.6fisher3896h5 LM03Fsh3896 22.7 26.6 18.5 24.2 21.1.
  9. Cluster Voting for Speaker Diarisation S.E.…

    mi.eng.cam.ac.uk/reports/svr-ftp/tranter_tr476.pdf
    13 May 2004: 92 32.06 24.95Num Poss 27 9 3 (39)Best 19.94 36.46 31.74 24.68. ... miss Worst 31.96 37.96 32.06 27.44allowed Mean 24.63 37.45 31.95 25.83.
  10. Experiments with Fisher Data Gunnar Evermann, Bin Jia, Kai ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/evermann_sttmay04.pdf
    25 May 2004: System MPE Prior eval03 Male Female. MPE-GI Dynamic MMI 25.9 27.3 24.5MPE-GD MPE-GI model 25.6 27.1 24.1. ... Gaussianised 29.8 21.9 26.0CN 28.7 21.3 25.1. CNC 28.1 20.8 24.6. •
  11. paper.dvi

    mi.eng.cam.ac.uk/reports/svr-ftp/cipolla_bmvc04.pdf
    28 Jul 2004: 4] M. Fischler and R. Bolles. Random sample consensus: A paradigm for model fitting with applications to image analysisand automated cartography.Graphics and Image Processing, 24(6):381–395, 1981.

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