set a smaller beam for pruned lattice rescoring with kaldi-rnnlm#2325
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danpovey merged 2 commits intokaldi-asr:masterfrom Apr 13, 2018
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set a smaller beam for pruned lattice rescoring with kaldi-rnnlm#2325danpovey merged 2 commits intokaldi-asr:masterfrom
danpovey merged 2 commits intokaldi-asr:masterfrom
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danpovey
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Mar 30, 2018
| # as in our RNNLM setup, a properly trained network would automatically | ||
| # have its normalization term close to 1. The details of this | ||
| # could be found at http://www.danielpovey.com/files/2018_icassp_rnnlm.pdf | ||
| lattice_prune_beam=4 # Beam used in pruned lattice composition |
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@hainan-xv, in your experience, how much difference does this make to WER results? (4 vs. 6)
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I just ran some experiments and surprisingly, the final WER is very
insensitive to the beam-size -
I tried beam = 0,1,2,4,6 and the results are all the same.
I suspected there is a bug but the results are better than the baseline,
also the resulting lattices have
different sizes. So maybe it's no problem.
…On Fri, Mar 30, 2018 at 2:35 PM, Daniel Povey ***@***.***> wrote:
***@***.**** commented on this pull request.
------------------------------
In scripts/rnnlm/lmrescore_pruned.sh
<#2325 (comment)>:
> @@ -26,6 +26,8 @@ normalize=false # If true, we add a normalization step to the output of the RNNL
# as in our RNNLM setup, a properly trained network would automatically
# have its normalization term close to 1. The details of this
# could be found at http://www.danielpovey.com/files/2018_icassp_rnnlm.pdf
+lattice_prune_beam=4 # Beam used in pruned lattice composition
@hainan-xv <https://github.com/hainan-xv>, in your experience, how much
difference does this make to WER results? (4 vs. 6)
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…take effect for small-sized lattices
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