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  2. EARS STT Overview Phil Woodland February 4th 2004 Cambridge ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/woodland_board_earsfeb04.pdf
    23 Mar 2004: Key Issues: 2004 Data. • New data planned for collection in 2004 includes– more Fisher English CTS data– 200 hours of Levantine Arabic CTS data– 200 hours of Mandarin CTS data– English
  3. RF and amplitude-basedprobe pressure correction for 3D ultrasound…

    mi.eng.cam.ac.uk/reports/svr-ftp/treece_tr491.pdf
    24 Aug 2004: 2 SYSTEM 4. 0 50 100 150 200 250 300 350 4001.5. ... 1 0.5 0 0.5 1500. 450. 400. 350. 300. 250. 200.
  4. 23 Dec 2004: A class based trigram language model was alsobuilt on the acoustic sources, using 200 classes estimated fromall the acoustic training data.
  5. Experiments with Fisher Data Gunnar Evermann, Bin Jia, Kai ...

    mi.eng.cam.ac.uk/research/projects/EARS/pubs/evermann_sttmay04.pdf
    25 May 2004: Gaussianisation. • Transform any distribution to standard Gaussian N(0, I). 20 15 10 5 0 5 10 15 200. ... 0.01. 0.02. 0.03. 0.04. 0.05. 0.06. 0.07. 0.08. 0.09. 0.1. 20 15 10 5 0 5 10 15 200.
  6. 3D ULTRASOUND PROBECALIBRATION WITHOUT A POSITION SENSOR A. H. ...

    mi.eng.cam.ac.uk/reports/svr-ftp/gee_tr488.pdf
    4 Sep 2004: Despite this, the wedge segmentation algorithm is quite capable ofdetecting misalignments of the order of 200 µm. ... 3. 2. 6. 7. 4. 10. 3. 2. 6. (a) Aligned (b) 200 µm (c) 400 µm.
  7. 23 Dec 2004: TheCallHome English (CHE) corpus consists of a total of 200 conversations between family membersand close friends, no restriction was placed on the conversation topics. ... Criterion Training set τ I train eval98ML h5train00sub — 47.8 46.5MMI
  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: 0.8. 1. 1.2. 1.4. 1.6. 0 200 400 600 800 1000.
  9. Cluster Voting for Speaker Diarisation S.E.…

    mi.eng.cam.ac.uk/reports/svr-ftp/tranter_tr476.pdf
    13 May 2004: Cluster Voting for Speaker Diarisation. S.E. TranterCUED/F-INFENG/TR-476. 1st May 2004. Abstract:. It is often important to be able to automatically detect ‘who spoke when’ in audio data. The speaker di-arisation task attempts to address this
  10. 26 Jan 2004: CAMBRIDGE UNIVERSITYENGINEERING DEPARTMENT. GENERALISEDLINEAR GAUSSIAN MODELS. A-V.I. Rosti & M.J.F. GalesCUED/F-INFENG/TR.420. November 23, 2001. Cambridge University Engineering DepartmentTrumpington Street. Cambridge. CB2 1PZEngland. E-mail:
  11. Linear Gaussian Models for Speech Recognition

    mi.eng.cam.ac.uk/reports/svr-ftp/rosti_thesis.pdf
    8 Oct 2004: Linear Gaussian Models for. Speech Recognition. Antti-Veikko Ilmari Rosti. Wolfson College. May 2004. Dissertation submitted to the University of Cambridge. for the degree of Doctor of Philosophy. ii. Declaration. This dissertation is the result of

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