Commit 4dd5597d authored by Dan Povey's avatar Dan Povey
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Improvements to ICASSP paper.. nearly finished.

git-svn-id: 5e6a8d80-dfce-4ca6-a32a-6e07a63d50c8
parent e68e0303
......@@ -87,7 +87,8 @@
\name{ \em Daniel Povey$^1$, Mirko Hannemann$^{1,2}$, \\
\em {Gilles Boulianne}$^3$, {Luk\'{a}\v{s} Burget}$^4$, {Arnab Ghoshal}$^5$, {Milos Janda}$^2$, {Stefan Kombrink}$^2$, \\
\em {Petr Motl\'{i}\v{c}ek}$^6$, {Yanmin Qian}$^7$, {Ngoc Thang Vu}$^8$, {Korbinian Riedhammer}$^9$, {Karel Vesel\'{y}}$^2$
\thanks{Thanks here.. remember Sanjeev.} }
\thanks{Thanks to Sanjeev Khudanpur for his help in preparing the paper}}
%%% TODO: fix thanks.
%\thanks{Arnab Ghoshal was supported by the European Community's Seventh Framework
......@@ -165,8 +166,10 @@ symbols, the special label $\epsilon$ may appear meaning ``no label is present.'
\caption{Acceptor $U$ describing the acoustic scores of an utterance}
Imagine we have an utterance, of length $T$, and want to ``decode'' it (i.e. find
......@@ -452,8 +455,8 @@ to a state index. We define a {\em minimial representation} of a state
to be like the canonical representation, but only keeping states that
are either final, or have non-$\epsilon$ arcs out of them. We maintain
a map from the minimal representation to the state index. We can
show that this algorithm is still correct (it will tend to give output
with fewer states, i.e. more minimal). As an optimization for speed, we also define
show that this algorithm is still correct (it will tend to give more
minimal output). As an optimization for speed, we also define
the {\em initial representation} to be the same type of subset, but prior
to following through the $\epsilon$ arcs, i.e. it only contains the states
that we reached by following non-$\epsilon$ arcs from a previous determinized
......@@ -513,14 +516,42 @@ read speech.
Our system is a standard mixture-of-Gaussians system trained on the SI-284
training data; we test on the November 1992 evaluation data.
For these experiments we generate lattices with the bigram language model
supplied with the WSJ database, and rescore them with the trigram language model.
supplied with the WSJ database, and for rescoring experiments we
use the trigram language model. The acoustic scale was $1/16$ for first-pass
decoding and $1/15$ for LM rescoring.
For simplicity, our results were all obtained using a decoder that
does not support a ``maximum active states'' option, so the only variables
to consider are the beam used in the Viterbi beam search, and the separate
beam $\alpha$ used for lattice generation.
\caption{\vspace*{-0.2in} Varying lattice beam $\alpha$ (Viterbi pruning beam fixed at 15) }
Figure~\ref{fig:latbeam} shows how various quantities change as we vary
$\alpha$, with the Viterbi beam fixed at 15. Note that we get all the improvement from
LM rescoring by increasing $\alpha$ to 4. The time taken by our algorithm started to increase rapidly after about
$\alpha=8$, so a value of $\alpha$ anywhere between about 4 and 8 is sufficient for LM rescoring
and still does not slow down decoding too much.
Note that out of vocabularly words (OOVs) provide a floor on the lattice oracle error rate.
Of the 333 test utterances, 87 contained an OOV word, yet only 93 sentences had oracle errors
with $\alpha=10$. Figure~\ref{fig:viterbibeam} shows the effect of varying the
Viterbi decoding beam, while leaving $\alpha$ fixed at 7.
\caption{\vspace*{-0.2in} Varying Viterbi pruning beam (lattice beam $\alpha$ fixed at 7) }
......@@ -528,7 +559,11 @@ supplied with the WSJ database, and rescore them with the trigram language model
Conclusions here [mention does not rely on word-pair assumption]
We have described a lattice generation method that is to our knowledge the first
efficient method not to rely on the word-pair assumption of~\cite{ney_word_graph}.
It includes an ingenious way of obtaining state-level alignment information
via determinization in a specially designed semiring.
......@@ -20,7 +20,7 @@
title={The use of context in large vocabulary speech recognition},
author={Odell, J.J.},
publisher={Cambridge University Engineering Dept.}
school={Cambridge University Engineering Dept.}
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