| Ideas for accelerating PTM computation |
| -------------------------------------- |
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| First thing to note is that codebook computation now takes up the |
| majority of the time spent evaluating PTMs. So speeding up Gaussian |
| evaluation is suddenly important again. |
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| Using a tighter top-N beam will speed up Gaussian computation by |
| imposing a higher floor on densities, but this effect isn't worth a |
| whole lot, in contrast to SC models where mixture computation rather |
| than density computation is the most expensive part. |
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| This means that we should probably bring back kd-trees, although the |
| implementation should be tweaked to be faster loading. |
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| Also, maybe more importantly, we can do some form of CI-GMM selection |
| on the codebooks. This won't actually work with the way the models |
| are set up currently since the CI phones share the same codebook as |
| the CD ones, and the goal is to prune codebooks rather than phones. |
|
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| But wait! It's okay, because we still have the same top-N mechanism |
| as before. We can use those top-N scores to do early pruning of |
| entire codebooks. This ought to give us the most bang for the buck. |
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