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-- Main.simonk - 15 Feb 2007

Barbara Forbes : articulatory feature recognition

Goals

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

Background

Add some references here

Method

Data

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
    • 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)

Tools

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)

Results

Insert tables here!

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Topic revision: r2 - 16 Feb 2007 - 10:23:47 - SimonKing
 
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