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-- Main.simonk - 15 Feb 2007

Barbara Forbes : articulatory feature recognition


Test a new feature representation by training ANNs to detect in speech, then compare to previous experiments using other feature systems


Add some references here



TIMIT is at /group/corpora/public/timit/original

Important note about TIMIT: do NOT use the sa1 and sa2 files (the 'shibboleth' utterances); only use the sx and si utterances (8 of these in total per speaker)

Project workspace is at /group/cstr/projects/dbns/v1bforbe

Preparing the data:

  • parameterise waveforms as PLPs, put in Quicknet format (a 'pfile')
    • Joe will do this
  • TIMIT labels -> Quicknet label files Here are master label files (mlfs) for both the original 61 phone set, and the standard reduced 39 phone set. These files are archives of all the labels.

    • Step 1: collapse phone labels down to feature labels (do this for each individual feature) - write a Python script to do this
    • Step 2: convert these collapsed labels files into Quicknet targets (Joe will do this)


Start with Quicknet. We might also try Nico if time permits.

  • Quicknet
    • Joe will add info here
  • Nico
    • Mirjam will add detail here

Training the nets

  • Quicknet version
    • There will be one net per feature; it will have two outouts, one for "feature=1" and the other for "feature=0"
    • Softmax over these outputs

Computing accuracy

1) Framewise accuracy: just count all frames where all features were correct (also compute results per-feature)

2) Mapped-to-phones framewise accuracy: map each phone to the nearest (Euclidean distance) valid feature combination, then compute as above

3) Allowing for timing errors: allow a "collar" around phone boundaries when scoring (e.g. ignore those frames)


Insert tables here!

Topic attachments
I Attachment Action Size Date Who Comment
elsemlf timit.mlf manage 3927.2 K 16 Feb 2007 - 10:31 Main.joe  
elsemlf timit39.mlf manage 2805.3 K 16 Feb 2007 - 10:31 Main.joe  
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Topic revision: r3 - 16 Feb 2007 - 10:33:28 - Main.joe
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