nbest-mix.1

这是一款很好用的工具包

.\" $Id: nbest-mix.1,v 1.6 2004/12/03 17:59:01 stolcke Exp $
.TH nbest-mix 1 "$Date: 2004/12/03 17:59:01 $" "SRILM Tools"
.SH NAME
nbest-mix \- interpolate N-best posterior probabilities
.SH SYNOPSIS
.B nbest-mix
[\c
.BR \-help ]
option
\&...
.I weight1
.I nbest1
.I weight2
.I nbest2 
\&...
.SH DESCRIPTION
.B nbest-mix
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.
.PP
The command line arguments form an alternating list of 
weight values and N-best file names.
.SH OPTIONS
.PP
Each filename argument can be an ASCII file, or a 
compressed file (name ending in .Z or .gz), or ``-'' to indicate
stdin/stdout.
.TP
.B \-help
Print option summary.
.TP
.B \-version
Print version information.
.TP
.BI \-debug " level"
Controls the amount of output (the higher the
.IR level ,
the more).
.TP
.BI \-write-nbest " file"
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.
.TP
.BI \-max-nbest " n"
Limits the number of hypotheses read from each N-best list to the first
.IR n .
.TP
.BI \-rescore-lmw " lmw"
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).
.TP
.BI \-rescore-wtw " wtw"
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).
.TP
.BI \-posterior-scale " scale"
Divide the total weighted log score by 
.I scale
when computing normalized posterior probabilities.
This controls the peakedness of the posterior distribution. 
The default value is whatever was chosen for 
.IR lmw ,
so that language model scores are scaled to have weight 1,
and acoustic scores have weight 1/\fIlmw\fP.
.TP
.B \-set-lm-scores
In conjunction with
.BR \-write-nbest ,
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
.BR nbest-optimize (1).
.TP
.B \-set-am-scores
Analogous to 
.BR \-set-lm-scores ,
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.
.SH "SEE ALSO"
nbest-lattice(1), nbest-scripts(1), nbest-optimize(1).
.br
A. Stolcke, K. Ries, N. Coccaro, E. Shriberg, R. Bates, D. Jurafsky, P. Taylor,
R. Martin, C. Van Ess-Dykema, & M. Meteer,
``Dialogue Act Modeling for Automatic Tagging and Recognition of Conversational
Speech,''
\fIComputational Linguistics\fP 26(3), 339-373, 2000.
.SH BUGS
Hopefully not.
.SH AUTHOR
Andreas Stolcke <[e-mail]>.
.br
Copyright 1998\-2004 SRI International