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SimonKing - 25 Jul 2006
Svitchboard, monophone, hybrid
This system uses the 8 ANNs to provide virtual evidence about the 8 features. The 8 feature hidden RVs each depend on the phone state using a
DenseCPT
| Vocab size | Task | Word error rate (%) | VE scale factors | language model | Notes |
| | | Validation | Test | dg1 | pl1 | scale | penalty | |
| 10 | 1 | 33.7 | | 1.0 | 1.0 | 20 | -3 | full D set |
| 32.5 | | 0.5 | 1.0 | 20 | -2 |
| 29.0 | 35.1 | 0.5 | 1.5 | 22 | -2 | searching over 0.1,0.5,1,1.5,2,4,8,16 for each of dg1 and pl1 scale factors |
| 500 | 84.6 | | 0.5 | 1.5 | 20 | -1 | ckbeam 10000, NOT TUNED recipe 1 |
Validation means the D_short set, unless noted.
Recipes for the 500 word task
Very slow to train starting with uniform DCPTs (unless I can find a better triangulation), so:
Recipe 1
Train on 1000 utterances for 2 iterations
Take the DCPTs and make them more sparse by zeroing all entries less than 0.1
Using these parameters, run the genetic triangulation script to find a fast triangulation, given this particular sparsity of the DCPTs.
Starting from these parameters, train to 0.5% tolerance (takes 8 its) on full training set
Find a decoding graph triangulation using the final trained parameters.
