--- /dev/null
+#!/usr/bin/perl
+
+# NOTE: this is by no means an efficient implementation and performance will
+# deteriorate rapidly as a function of the corpus size. Larger corpora should be
+# processed using the toolkit available at http://www.speech.cs.cmu.edu/SLM_info.html
+
+# [2feb96] (air)
+# cobbles together a language model from a set of exemplar sentences.
+# features: 1) uniform discounting, 2) no cutoffs
+# the "+" version allows insertion of extra words into the 1gram vector
+
+# [27nov97] (air)
+# bulletproof a bit for use in conjunction with a cgi script
+
+# [20000711] (air)
+# made visible the discount parmeter
+
+# [20011123] (air)
+# cleaned-up version for distribution
+
+use Getopt::Std;
+
+$VERBOSE = 1;
+
+sub handler { local($sig) = @_;
+ print STDERR "quick_lm caught a SIG$sig -- dying\n";
+ exit(0);
+ }
+foreach (qw(XCPU KILL TERM STOP)) { $SIG{$_} = \&handler; }
+
+
+if ($#ARGV < 0) { die("usage: quick_lm -s <sentence_file> -o <output_file> [-w <word_file>] [-d discount]\n"); }
+Getopt::Std::getopts("s:w:d:o:x");
+$sentfile = $opt_s;
+$wordfile = $opt_w;
+$discount = $opt_d;
+$output = $opt_o;
+
+$output or die("No output file\n");
+$sentfile or die("No sentence file\n");
+
+$| = 1; # always flush buffers
+
+if ($VERBOSE>0) {print STDERR "Language model started at ",scalar localtime(),"\n";}
+
+
+open(IN,"<$sentfile") or die("can't open $sentfile!\n");
+if ($wordfile ne "") { open(WORDS,"$wordfile"); $wflag = 1;} else { $wflag = 0; }
+
+$log10 = log(10.0);
+
+if ($discount ne "") {
+ if (($discount<=0.0) or ($discount>=1.0)) {
+ print STDERR "\discount value out of range: must be 0.0 < x < 1.0! ...using 0.5\n";
+ $discount_mass = 0.5; # just use default
+ } else {
+ $discount_mass = $discount;
+ }
+} else {
+ # Ben and Greg's experiments show that 0.5 is a way better default choice.
+ $discount_mass = 0.5; # Set a nominal discount...
+}
+$deflator = 1.0 - $discount_mass;
+
+# create count tables
+$sent_cnt = 0;
+while (<IN>) {
+ s/^\s*//; s/\s*$//;
+ if ( $_ eq "" ) { next; } else { $sent_cnt++; } # skip empty lines
+ @word = split(/\s/);
+ for ($j=0;$j<($#word-1);$j++) {
+ $trigram{join(" ",$word[$j],$word[$j+1],$word[$j+2])}++;
+ $bigram{ join(" ",$word[$j],$word[$j+1])}++;
+ $unigram{$word[$j]}++;
+ }
+ # finish up the bi and uni's at the end of the sentence...
+ $bigram{join(" ",$word[$j],$word[$j+1])}++;
+ $unigram{$word[$j]}++;
+
+ $unigram{$word[$j+1]}++;
+}
+close(IN);
+if ($VERBOSE) { print STDERR "$sent_cnt sentences found.\n"; }
+
+# add in any words
+if ($wflag) {
+ $new = 0; $read_in = 0;
+ while (<WORDS>) {
+ s/^\s*//; s/\s*$//;
+ if ( $_ eq "" ) { next; } else { $read_in++; } # skip empty lines
+ if (! $unigram{$_}) { $unigram{$_} = 1; $new++; }
+ }
+ if ($VERBOSE) { print STDERR "tried to add $read_in word; $new were new words\n"; }
+ close (WORDS);
+}
+if ( ($sent_cnt==0) && ($new==0) ) {
+ print STDERR "no input?\n";
+ exit;
+}
+
+open(LM,">$output") or die("can't open $myfile.lm for output!\n");
+
+$preface = "";
+$preface .= "Language model created by QuickLM on ".`date`;
+$preface .= "Copyright (c) 1996-2002\nCarnegie Mellon University and Alexander I. Rudnicky\n\n";
+$preface .= "This model based on a corpus of $sent_cnt sentences and ".scalar (keys %unigram). " words\n";
+$preface .= "The (fixed) discount mass is $discount_mass\n\n";
+
+
+# compute counts
+$unisum = 0; $uni_count = 0; $bi_count = 0; $tri_count = 0;
+foreach $x (keys(%unigram)) { $uni_count++; $unisum += $unigram{$x}; }
+foreach $x (keys(%bigram)) { $bi_count++; }
+foreach $x (keys(%trigram)) { $tri_count++; }
+
+print LM $preface;
+print LM "\\data\\\n";
+print LM "ngram 1=$uni_count\n";
+if ( $bi_count > 0 ) { print LM "ngram 2=$bi_count\n"; }
+if ( $tri_count > 0 ) { print LM "ngram 3=$tri_count\n"; }
+print LM "\n";
+
+# compute uni probs
+foreach $x (keys(%unigram)) {
+ $uniprob{$x} = ($unigram{$x}/$unisum) * $deflator;
+}
+
+# compute alphas
+foreach $y (keys(%unigram)) {
+ $w1 = $y;
+ $sum_denom = 0.0;
+ foreach $x (keys(%bigram)) {
+ if ( substr($x,0,rindex($x," ")) eq $w1 ) {
+ $w2 = substr($x,index($x," ")+1);
+ $sum_denom += $uniprob{$w2};
+ }
+ }
+ $alpha{$w1} = $discount_mass / (1.0 - $sum_denom);
+}
+
+print LM "\\1-grams:\n";
+foreach $x (sort keys(%unigram)) {
+ printf LM "%6.4f %s %6.4f\n", log($uniprob{$x})/$log10, $x, log($alpha{$x})/$log10;
+}
+print LM "\n";
+
+#compute bi probs
+foreach $x (keys(%bigram)) {
+ $w1 = substr($x,0,rindex($x," "));
+ $biprob{$x} = ($bigram{$x}*$deflator)/$unigram{$w1};
+}
+
+#compute bialphas
+foreach $x (keys(%bigram)) {
+ $w1w2 = $x;
+ $sum_denom = 0.0;
+ foreach $y (keys(%trigram)) {
+ if (substr($y,0,rindex($y," ")) eq $w1w2 ) {
+ $w2w3 = substr($y,index($y," ")+1);
+ $sum_denom += $biprob{$w2w3};
+ }
+ }
+ $bialpha{$w1w2} = $discount_mass / (1.0 - $sum_denom);
+}
+
+# output the bigrams and trigrams (now that we have the alphas computed).
+if ( $bi_count > 0 ) {
+ print LM "\\2-grams:\n";
+ foreach $x (sort keys(%bigram)) {
+ printf LM "%6.4f %s %6.4f\n",
+ log($biprob{$x})/$log10, $x, log($bialpha{$x})/$log10;
+ }
+ print LM "\n";
+}
+
+if ($tri_count > 0 ) {
+ print LM "\\3-grams:\n";
+ foreach $x (sort keys(%trigram)) {
+ $w1w2 = substr($x,0,rindex($x," "));
+ printf LM "%6.4f %s\n",
+ log(($trigram{$x}*$deflator)/$bigram{$w1w2})/$log10, $x;
+ }
+ print LM "\n";
+}
+
+print LM "\\end\\\n";
+close(LM);
+
+if ($VERBOSE>0) { print STDERR "Language model completed at ",scalar localtime(),"\n"; }
+
+#
+__END__
+=pod
+
+/* ====================================================================
+ * Copyright (c) 1996-2002 Alexander I. Rudnicky and Carnegie Mellon University.
+ * All rights reserved.
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions
+ * are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright
+ * notice, this list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright
+ * notice, this list of conditions and the following disclaimer in
+ * the documentation and/or other materials provided with the
+ * distribution.
+ *
+ * 3. All copies, used or distributed, must preserve the original wording of
+ * the copyright notice included in the output file.
+ *
+ * This work was supported in part by funding from the Defense Advanced
+ * Research Projects Agency and the CMU Sphinx Speech Consortium.
+ *
+ * THIS SOFTWARE IS PROVIDED BY CARNEGIE MELLON UNIVERSITY ``AS IS'' AND
+ * ANY EXPRESSED OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
+ * THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
+ * PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL CARNEGIE MELLON UNIVERSITY
+ * NOR ITS EMPLOYEES BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+ * SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+ * LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+ * DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+ * THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+ * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ * ====================================================================
+ *
+ */
+
+
+Pretty Good Language Modeler, now with unigram vector augmentation!
+
+The Pretty Good Language Modeler is intended for quick construction of small
+language models, typically as might be needed in application development. Depending
+on the version of Perl that you are running, a practical limitation is a
+maximum vocabulary size on the order of 1000-2000 words. The limiting factor
+is the number of n-grams observed, since each n-gram is stored as a hash key.
+(So smaller vocabularies may turn out to be a problem as well.)
+
+This package computes a stadard back-off language model. It differs in one significant
+respect, which is the computation of the discount. We adopt a "proportional" (or ratio)
+discount in which a certain percentage of probability mass is removed (typically 50%)
+from observed n-grams and redistributed over unobserved n-grams.
+
+Conventionally, an absolute discount would be used, however we have found that the
+proportional discount appears to be robust for extremely small languages, as might be
+prototyped by a developer, as opposed to based on a collected corpus. We have found that
+absolute and proportional discounts produce comparable recognition results with perhaps
+a slight advantage for proportional discounting. A more systematic investigation of
+this technique would be desirable. In any case it also has the virtue of using a very
+simple computation.
+
+=end
+
projects / thirdparty / freeswitch.git / commitdiff
