[SRILM User List] SRILM trigram worse than HTK bigram?

[Andreas Stolcke]

You need to run a few sanity checks to make sure things are working as 
you expect them to.

1.  Decode 1-best from the HTK lattice WITHOUT rescoring.  The results 
should be the same as from the HTK decoder.  If not there might be a 
difference in the LM scaling factor, and you may have to adjust is via 
the command line option. There might also be issues with the CTM output 
and conversion back to MLF.

2. Rescore the lattices with the same LM that is used in the HTK 
decoder.   Again, the results should be essentially identical.
I'm not familiar with the bigram format used by HTK, but you may have to 
convert it to ARPA format.

3. Then try rescoring with a trigram.

Approaching your goal in steps hopefully will help you pinpoint the 
problem(s).

Andreas

On 11/22/2012 5:06 AM, Dmytro Prylipko wrote:
> Hi,
>
> I found that the accuracy of the recognition results obtained with 
> HVite is about 5% better with comparison to the hypothesis got after 
> rescoring the lattices with lattice-tool.
>
> HVite do not really use an N-gram, it is a word net, but I cannot 
> really figure out why does it work so much better than SRILM models.
>
> I use the following script to generate lattices (60-best):
>
> HVite -A -T 1 \
> -C GENLATTICES.conf \
> -n 20 60 \
> -l outLatDir \
> -z lat \
> -H hmmDefs \
> -S test.list \
> -i out.bigram.HLStats.mlf \
> -w bigram.HLStats.lat \
> -p 0.0 \
> -s 8.0 \
> lexicon \
> hmm.mono.list
>
> Which are then rescored with:
>
> lattice-tool \
> -read-htk \
> -write-htk \
> -htk-lmscale 10.0 \
> -htk-words-on-nodes \
> -order 3 \
> -in-lattice-list srclat.list \
> -out-lattice-dir rescoredLatDir \
> -lm trigram.SRILM.lm \
> -overwrite
>
> find rescoredLatDir -name "*.lat" > rescoredLat.list
>
> lattice-tool \
> -read-htk \
> -write-htk \
> -htk-lmscale 10.0 \
> -htk-words-on-nodes \
> -order 3  \
> -in-lattice-list rescoredLat.list\
> -viterbi-decode \
> -output-ctm | ctm2mlf_r > out.trigram.SRILM.mlf
>
> Decoded with HVite (92.86%):
>
>  LAB: <A> wie sieht es aus mit einem weiteren zweitaegigen mit einer 
> weiteren zweitaegigen arbeitssitzu
>  REC: <A> wie sieht es aus mit einem weiteren zweitaegigen in  einer 
> weiteren zweitaegigen arbeitssitzu
>
> ... and with lattice-tool (64.29%):
>
>  LAB: <A> wie sieht es aus mit einem weiteren zweitaegigen mit  einer 
> weiteren zweitaegigen arbeitssitzu
>  REC: <A> wie sieht es aus mit einen weiteren zweitaegigen dann bei   
> einem    zweitaegigen arbeitssitzung
>
> Corresponding word nets and LMs have been built using the same 
> vocabulary and training data. I should say that for some sentences 
> SRILM outperforms HTK, but in general it is roughly 5-7% behind.
> Could you please suggest why is it so? Maybe some parameter values are 
> wrong?
> Or should it be like this?
>
> I would be greatly appreciated for help.
>
> Yours,
> Dmytro Prylipko.
>
>
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