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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: For non-English. • Improvements for all languages / tasks. • Relatively little data for non-English. • ... STT Collaboration Examples. • Sharing & Preparing Data– Wordwave segmentations (BBN)– Common broadcast news development sets (LIMSI
  3. Speaker Diarisation for Broadcast News

    mi.eng.cam.ac.uk/reports/full_html/tranter_odyssey04.html/
    14 Jun 2004: The. CU_TDT4. system removed 18.4% of the audio, which consisted of 1783s=86.3% of all the adverts and 198s=2.28% of all the news; whilst the. ... data. The system removed 8.9% of the evaluation data, which consisted of 867s=40.5% of all the adverts and
  4. 23 Dec 2004: Table 5. Performance varying the decision tree and HLDA trainingdata, all models MPE trained. ... All experiments use the interpolated language modelwith the news corpora. Language Model System (S3) CER (%)dev04.
  5. Speaker Diarisation for Broadcast News S. E. Tranter† and ...

    mi.eng.cam.ac.uk/reports/svr-ftp/tranter_odyssey04.pdf
    25 Mar 2004: The CU TDT4 system re-moved 18.4% of the audio, which consisted of 1783s=86.3%of all the adverts and 198s=2.28% of all the news; whilst theCU ... The system removed8.9% of the evaluation data, which consisted of 867s=40.5% ofall the adverts and 70s=0.83%
  6. Cluster Voting for Speaker Diarisation S.E.…

    mi.eng.cam.ac.uk/reports/svr-ftp/tranter_tr476.pdf
    13 May 2004: 32.2 The Broadcast News Data for Diarisation. 32.3 Diarisation Scoring. 4. ... For this case all the data is given a speaker label (i.e.

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