Files
b2txt25/language_model/srilm-1.7.3/man/html/nbest-mix.1.html
2025-07-02 12:18:09 -07:00

112 lines
4.3 KiB
HTML

<! $Id: nbest-mix.1,v 1.8 2019/09/09 22:35:37 stolcke Exp $>
<HTML>
<HEADER>
<TITLE>nbest-mix</TITLE>
<BODY>
<H1>nbest-mix</H1>
<H2> NAME </H2>
nbest-mix - interpolate N-best posterior probabilities
<H2> SYNOPSIS </H2>
<B>nbest-mix</B> [ <B>-help</B> ] <I>option</I> ... <I> weight1 nbest1 weight2 nbest2</I> ...
</PRE>
<H2> DESCRIPTION </H2>
<B> nbest-mix </B>
reads a number of N-best lists (which must contain identical
hypotheses), computes the hypothesis posterior probabilities for each,
and computes a new posterior distribution that is a
weighted mixture of the input distributions.
The hypothesis with the highest combined posterior probability is
printed.
<P>
The command line arguments form an alternating list of
weight values and N-best file names.
<H2> OPTIONS </H2>
<P>
Each filename argument can be an ASCII file, or a
compressed file (name ending in .Z or .gz), or ``-'' to indicate
stdin/stdout.
<DL>
<DT><B> -help </B>
<DD>
Print option summary.
<DT><B> -version </B>
<DD>
Print version information.
<DT><B>-debug</B><I> level</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Controls the amount of output (the higher the
<I>level</I>,<I></I><I></I><I></I>
the more).
<DT><B>-write-nbest</B><I> file</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Output N-best lists containing scores that correspond to the log of
the combined posteriors of the input hypotheses.
The log posterior is assigned as the acoustic score and other scores
are set to zero.
This also suppresses the printing of the best hyp.
<DT><B>-max-nbest</B><I> n</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Limits the number of hypotheses read from each N-best list to the first
<I>n</I>.<I></I><I></I><I></I>
<DT><B>-rescore-lmw</B><I> lmw</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Sets the language model weight used in combining the language model log
probabilities with acoustic log probabilities
(only relevant if separate scores are given in the N-best input).
<DT><B>-rescore-wtw</B><I> wtw</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Sets the word transition weight used to weight the number of words relative to
the acoustic log probabilities
(only relevant if separate scores are given in the N-best input).
<DT><B>-posterior-scale</B><I> scale</I><B></B><I></I><B></B><I></I><B></B>
<DD>
Divide the total weighted log score by
<I> scale </I>
when computing normalized posterior probabilities.
This controls the peakedness of the posterior distribution.
The default value is whatever was chosen for
<I>lmw</I>,<I></I><I></I><I></I>
so that language model scores are scaled to have weight 1,
and acoustic scores have weight 1/<I>lmw</I>.
<DT><B> -set-lm-scores </B>
<DD>
In conjunction with
<B>-write-nbest</B>,<B></B><B></B><B></B>
output N-best lists that preserve the acoustic scores and word counts
of the (first of the) input N-best lists, and encodes the combined
log posteriors via the LM scores.
The LM scores in the output are calculated so that, when combined with
the acoustic scores and insertion penalties (using the given LM weight
and posterior scaling), the result is the weighted, combined posteriors based
on all input N-best scores.
This option is useful if input N-best lists were created by rescoring with
different language models, and the output N-best lists are to be combined
with other scores or if the score weighting is to be optimized with
<A HREF="nbest-optimize.1.html">nbest-optimize(1)</A>.
<DT><B> -set-am-scores </B>
<DD>
Analogous to
<B>-set-lm-scores</B>,<B></B><B></B><B></B>
except that the acoustic scores are modified to reflect combined log posterior
probabiltities, and other scores are preserved.
This option is useful if input N-best lists were created by rescoring with
different acoustic models.
</DD>
</DL>
<H2> SEE ALSO </H2>
<A HREF="nbest-lattice.1.html">nbest-lattice(1)</A>, <A HREF="nbest-scripts.1.html">nbest-scripts(1)</A>, <A HREF="nbest-optimize.1.html">nbest-optimize(1)</A>.
<BR>
A. Stolcke, K. Ries, N. Coccaro, E. Shriberg, R. Bates, D. Jurafsky, P. Taylor,
R. Martin, C. Van Ess-Dykema, &amp; M. Meteer,
``Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational
Speech,''
<I>Computational Linguistics</I> 26(3), 339-373, 2000.
<H2> BUGS </H2>
Hopefully not.
<H2> AUTHOR </H2>
Andreas Stolcke &lt;stolcke@icsi.berkeley.edu&gt;
<BR>
Copyright (c) 1998-2004 SRI International
</BODY>
</HTML>