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On 5/14/2012 4:41 AM, mvp-songyoung wrote:
<blockquote
cite="mid:660b26a4.15837.1374a82bf15.Coremail.mvp-songyoung@163.com"
type="cite">
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<div>Hi,I meet a question when lattice rescoring
with an interpolated class-based lm with
lattice-tool. This class-based LM was trained
by interpolating three other different
class-based LMs:LM1 c! ontian 3500 words and
merged into 350 clases;LM2 contain 2500 words
and merged into 250 classes ; LM3 contian 110
words and merged into 10 classes. I have
renamed the class definitions for three
class-based LMs before training and
interpolating them.and I also merged the class
definitions to a single file before decoding.
My decoding comand is as follows:</div>
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<div>lattice-tool -read-htk -viterbi-decode
-order 4 -lm class-4gram.lm -classes
<class> -in-lattice-list lattice.scp
-htk-wdpenalty $PENALTY -htk-lmscale $LMSCALE</div>
<div> </div>
<div>But, I found that the decoding process was
very slow and memory consuming. <font
face="Trebuchet MS">I wonder to know why I
meet and how to process this situation? Are
there any steps I have did incorrect? Please
give me the right steps? thank you</font></div>
<div><font face="Trebuchet MS"> !
&nbs
p; <br>
</font></div>
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</blockquote>
<br>
The -classes option leads to an LM that no longer uses only a finite
history to evaluate the probability of the next word. This means
that during lattice expansion all histories need to be kept
distinct. You should try the -simple-classes option, assuming your
models satisfy its requirements:<br>
<blockquote type="cite"><b><dt><b>-classes</b><i> file</i>
</dt>
<dd>
Interpret the LM as an N-gram over word classes.
The expansions of the classes are given in
<i>file</i>
in <a
href="http://www.speech.sri.com/projects/srilm/manpages/classes-format.5.html">classes-format(5)</a>.
Tokens in the LM that are not defined as classes in
<i> file </i>
are assumed to be plain words, so that the LM can contain
mixed N-grams over
both words and word classes.
</dd>
<dt><b>-simple-classes</b>
</dt>
<dd>
Assume a "simple" class model: each word is member of at most
one word class,
and class expansions are exactly one word long.
</dd>
</b></blockquote>
<br>
Hope this helps,<br>
<br>
Andreas<br>
<br>
<br>
<br>
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