exampsys.tex

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\centrefig{step6}{85}{Step 6}

The \texttt{-t} option sets the pruning\index{pruning} thresholds to be used during
training.  Pruning limits the range of state alignments that the
forward-backward algorithm includes in its summation and it
can reduce the amount of computation required by an
order of magnitude.  For most training files, a very tight pruning threshold
can be set, however, some training files will provide poorer acoustic
matching and in consequence a wider pruning beam is needed.  \htool{HERest}
deals with this by having an auto-incrementing pruning threshold.  In the
above example, pruning is normally 250.0.  If re-estimation fails on any
particular file, the threshold is increased by 150.0 and the file is
reprocessed.  This is repeated until either the file is successfully
processed or the pruning limit of 1000.0 is exceeded.  At this point it 
is safe to assume that there
is a serious problem with the training file and hence the fault should be fixed
(typically it will be an incorrect transcription) or the training file should be discarded.
The process leading to the initial set of monophones in the directory
\texttt{hmm0} is illustrated in Fig.~\href{f:step6}.

Each time \htool{HERest} is run it performs a single re-estimation.  Each new
HMM set is stored in a new directory.  Execution of \htool{HERest} should be
repeated twice more, changing the name of the input and output directories (set
with the options \texttt{-H} and \texttt{-M}) each time, until the directory
\texttt{hmm3} contains the final set of initialised monophone HMMs.

\subsection{Step 7 - Fixing the Silence Models}

\sidefig{egsils}{55}{Silence Models}{-4}{
The previous step has generated a 3 state left-to-right HMM for each
phone and also a HMM for the silence model\index{silence model} \texttt{sil}.  The 
next step is to add extra transitions from states 2 to 4 and from
states 4 to 2\index{transitions!adding them}
in the silence model.  The idea here is to make the model more robust
by allowing individual states to absorb the various
impulsive noises in the training data.  The backward skip allows this to happen
without committing the model to transit to the following word.

Also, at this point, a 1 state
short pause\index{short pause} \texttt{sp} model should be created.  
This should be a so-called \textit{tee-model}\index{tee-models}
which has a direct transition from entry to exit node.
This \texttt{sp} has its emitting state tied to the centre state of the silence model.
The required topology of the two silence models is shown in Fig.~\href{f:egsils}.
}

These silence models can be created in two stages
\begin{itemize}
\item 
Use a text editor on the file \texttt{hmm3/hmmdefs} to copy the centre state of
the \texttt{sil} model to
make a new \texttt{sp} model and store the resulting MMF \texttt{hmmdefs}, which 
includes the new \texttt{sp} model, in the new directory \texttt{hmm4}. 

\item Run the HMM editor \htool{HHEd}\index{hhed@\htool{HHEd}} to add 
the extra transitions required
and tie the \texttt{sp} state to the centre \texttt{sil} state
\end{itemize}

\htool{HHEd} works in a similar way to \htool{HLEd}.  It applies a set of commands in
a script to modify a set of HMMs.  In this case, it is executed as follows
\begin{verbatim}
    HHEd -H hmm4/macros -H hmm4/hmmdefs -M hmm5 sil.hed monophones1
\end{verbatim}
where \texttt{sil.hed} contains the following commands
\begin{verbatim}
    AT 2 4 0.2 {sil.transP}
    AT 4 2 0.2 {sil.transP}
    AT 1 3 0.3 {sp.transP}
    TI silst {sil.state[3],sp.state[2]}
\end{verbatim}
The \texttt{AT}\index{at@\texttt{AT} command} commands add transitions to the
given transition matrices and the final \texttt{TI}\index{ti@\texttt{TI}
command} command creates a tied-state called \texttt{silst}.  The parameters of
this tied-state are stored in the \texttt{hmmdefs} file and within each silence
model, the original state parameters are replaced by the name of this
macro\index{macros}.  Macros are described in more detail below. For now it is
sufficient to regard them simply as the mechanism by which
\HTK\ implements parameter sharing. 
Note that the phone list used here has been changed, because the original list
\texttt{monophones0} has been extended by the new \texttt{sp} model. The new 
file is called \texttt{monophones1} and has been used in the above \htool{HHEd}
command.

\centrefig{step7}{110}{Step 7}

Finally, another two passes of \htool{HERest} are applied using the phone
transcriptions with \texttt{sp} models between words.  This leaves the
set of monophone HMMs created so far in the directory \texttt{hmm7}.
This step is illustrated in Fig.~\href{f:step7}

\subsection{Step 8 - Realigning the Training Data}

As noted earlier, the dictionary contains multiple pronunciations for 
some words, particularly function words.  The phone models created so
far can be used to \textit{realign} the training data and create new
transcriptions.  This can be done with a single invocation of the\index{realignment}
\HTK\ recognition tool \htool{HVite}\index{hvite@\htool{HVite}}, viz
\begin{verbatim}
    HVite -l '*' -o SWT -b silence -C config -a -H hmm7/macros \
          -H hmm7/hmmdefs -i aligned.mlf -m -t 250.0 -y lab \
          -I words.mlf -S train.scp  dict monophones1 
\end{verbatim}
This command uses the HMMs stored in \texttt{hmm7} to transform the input
word level transcription \texttt{words.mlf} to the new phone level transcription
\texttt{aligned.mlf} using the pronunciations stored in the dictionary
\texttt{dict} (see Fig~\href{f:step8}).   The key difference between this
operation and the original word-to-phone mapping performed by \htool{HLEd}
in step 4 is that the recogniser considers all pronunciations for each
word and outputs the pronunciation that best matches the acoustic data.
\index{phone alignment}\index{phone mapping}

In the above, the \texttt{-b} option is used to insert a silence model\index{silence model}
at the start and end of each utterance.  The name \texttt{silence} is used
on the assumption that the dictionary contains an entry
\begin{verbatim}
    silence sil
\end{verbatim}
Note that the dictionary should be sorted firstly by case (upper case first) and secondly 
alphabetically.  The \texttt{-t} option sets a pruning level of 250.0 and the \texttt{-o} 
option is used to suppress the printing of scores, word names and time
boundaries in the output MLF.

\centrefig{step8}{85}{Step 8}

Once the new phone alignments have been created, another  2 passes
of \htool{HERest} can be applied to reestimate the HMM set parameters
again.  Assuming that this is done, the final monophone HMM set will
be stored in directory \texttt{hmm9}.

\mysect{Creating Tied-State Triphones}{egcreattri}

Given a set of monophone HMMs, the final stage of model building is to create
context-dependent triphone\index{HMM!triphones} HMMs.  This is done in 
two steps.  Firstly, the
monophone transcriptions are converted to triphone transcriptions and a set
of triphone models are created by copying the monophones and re-estimating.
Secondly, similar acoustic states of these triphones are tied to ensure that
all state distributions can be robustly estimated.

\subsection{Step 9 - Making Triphones from Monophones}

Context-dependent triphones can be made by simply 
cloning\index{HMM!cloning}\index{cloning} monophones and then
re-estimating using triphone transcriptions.  The latter should be created
first using \htool{HLEd}\index{hled@\htool{HLEd}} because 
a side-effect is to generate a list of all
the triphones for which there is at least one example in the training data.
That is, executing
\begin{verbatim}
    HLEd -n triphones1 -l '*' -i wintri.mlf mktri.led aligned.mlf
\end{verbatim}
will convert the monophone transcriptions in \texttt{aligned.mlf} to
an equivalent set of triphone transcriptions in \texttt{wintri.mlf}.
At the same time, a list of triphones is written to the file \texttt{triphones1}.
The edit script \texttt{mktri.led}  contains the commands
\begin{verbatim}
    WB sp
    WB sil
    TC 
\end{verbatim}
The two \texttt{WB}\index{wb@\texttt{WB} command} commands define \texttt{sp} and \texttt{sil}
as \textit{word boundary symbols}.  These then block the addition of
context in the \texttt{TI} command, seen in the following script, which converts all phones
(except word boundary symbols) to triphones
\index{triphones!word internal}\index{triphones!from monophones}\index{triphones!by cloning}.  
For example,
\begin{verbatim}
    sil th ih s sp m ae n sp ...
\end{verbatim}
becomes
\begin{verbatim}
    sil th+ih th-ih+s ih-s sp m+ae m-ae+n ae-n sp ...
\end{verbatim}
This style of triphone transcription is referred to as \textit{word internal}.
\index{word internal}
Note that some biphones will also be generated as contexts at word boundaries
will sometimes only include two phones.

The cloning of models can be done efficiently using the HMM editor \htool{HHEd}:
\begin{verbatim}
    HHEd -B -H hmm9/macros -H hmm9/hmmdefs -M hmm10 
         mktri.hed monophones1
\end{verbatim}
where the edit script \texttt{mktri.hed}
contains a clone command \texttt{CL} followed by \texttt{TI} commands to tie all of
the transition matrices in each triphone\index{triphones!notation} set, that is:
\begin{verbatim}
    CL triphones1
    TI T_ah {(*-ah+*,ah+*,*-ah).transP}
    TI T_ax {(*-ax+*,ax+*,*-ax).transP}
    TI T_ey {(*-ey+*,ey+*,*-ey).transP}
    TI T_b {(*-b+*,b+*,*-b).transP}
    TI T_ay {(*-ay+*,ay+*,*-ay).transP}
    ...
\end{verbatim}  
The file \texttt{mktri.hed} can be generated using the {\em Perl} script
\texttt{maketrihed} included in the \texttt{HTKTutorial} directory.
When running the \htool{HHEd}\index{hled@\htool{HHEd}} command you
will get warnings about trying to tie transition matrices for the sil
and sp models. Since neither model is context-dependent there aren't
actually any matrices to tie.

The clone command \texttt{CL}\index{cl@\texttt{CL} command} takes as its
argument the name of the file containing the list of triphones (and
biphones)\index{cloning}\index{parameter tying}\index{item lists} generated
above.  For each model of the form \texttt{a-b+c} in this list, it looks for
the monophone \texttt{b} and makes a copy of it.\index{tying!transition
matrices} Each \texttt{TI} command takes as its argument the name of a macro
and a list of HMM components.  The latter uses a notation which attempts to
mimic the hierarchical structure of the HMM parameter set in which the
transition matrix \texttt{transP} can be regarded as a sub-component of each
HMM.  The list of items within brackets are patterns designed to match the set
of triphones, right biphones and left biphones for each phone.

\centrefig{egtranstie}{80}{Tying Transition Matrices}

Up to now macros and tying have only been mentioned in passing.  Although a
full explanation must wait until chapter~\ref{c:HMMDefs}, a brief explanation
is warranted here.  Tying means that one or more HMMs share the same set of
parameters.  On the left side of Fig.~\href{f:egtranstie}, two HMM definitions
are shown.  Each HMM has its own individual transition matrix.  On the right
side, the effect of the first \texttt{TI} command in the edit script
\texttt{mktri.hed} is shown.  The individual transition matrices have been
replaced by a reference to a \textit{macro} called \texttt{T\_ah} which
contains a matrix shared by both models.  When reestimating tied parameters,
the data which would have been used for each of the original untied parameters
is pooled so that a much more reliable estimate can be obtained.

Of course, tying could affect performance if performed indiscriminately.
Hence, it is important to only tie parameters which have little effect on
discrimination.  This is the case here where the transition parameters do not
vary significantly with acoustic context but nevertheless need to be estimated
accurately.  Some triphones will occur only once or twice and so very poor
estimates would be obtained if tying was not done.  These problems of data
insufficiency will affect the output distributions too, but this will be dealt
with in the next step.

Hitherto, all HMMs have been stored in text format and could be inspected like
any text file.  Now however, the model files will be getting larger and space
and load/store times become an issue.  For increased efficiency,
\HTK\ can store and load MMFs in binary\index{HMM!binary storage}
format.  Setting the standard \texttt{-B} option causes this to happen.

\sidefig{step9}{55}{Step 9}{-4}{
Once the context-dependent models have been cloned, the new triphone set can be
re-estimated using \htool{HERest}.  This is done as previously except that the
monophone model list is replaced by a triphone list and the triphone
transcriptions are used in place of the monophone transcriptions.  

For the final pass of \htool{HERest}, the \texttt{-s} option should be used to
generate a file of state occupation statistics called \texttt{stats}.  In
combination with the means and variances, these enable likelihoods to be
calculated for clusters of states and are needed during the state-clustering
process \index{statistics!state occupation} described below.
Fig.~\href{f:step9} illustrates this step of the HMM construction
procedure. Re-estimation should be again done twice, so that the resultant
model sets will ultimately be saved in \texttt{hmm12}.  
}
\begin{verbatim}
   HERest -B -C config -I wintri.mlf -t 250.0 150.0 1000.0 -s stats \
    -S train.scp -H hmm11/macros -H hmm11/hmmdefs -M hmm12 triphones1
\end{verbatim}


\subsection{Step 10 - Making Tied-State Triphones}

The outcome of the previous stage is a set of triphone HMMs with all triphones
in a phone set sharing the same transition matrix.  When estimating these
models, many of the variances in the output distributions
will have been floored since there will be\index{variance!flooring problems}\index{state tying}
\index{tying!states}\index{data insufficiency}
insufficient data associated with many of the states.  The last step in
the model building process is to tie states within triphone sets
in order to share data and thus be able to make robust parameter estimates.

In the previous step, the \texttt{TI} command was used to
explicitly tie all members of a set of transition matrices together. 
However,
the choice of which states to tie requires a bit more  subtlety since
the performance of the recogniser depends crucially on how accurate
the state output distributions capture the statistics of the speech data.

\htool{HHEd} provides two mechanisms which allow states to be clustered 
and\index{state clustering}
then each cluster tied.  The first is data-driven and uses a similarity
measure between states.  The second uses decision trees\index{decision trees}
and is based on asking questions about the left and right contexts of each
triphone.  The decision tree attempts to find those contexts which make the largest
difference to the acoustics and which should therefore distinguish clusters.

Decision tree state tying is performed by running \htool{HHEd} 
in the normal way, i.e.
\begin{verbatim}
   HHEd -B -H hmm12/macros -H hmm12/hmmdefs -M hmm13 \
        tree.hed triphones1 > log
\end{verbatim}
Notice that the output is saved in a log file.  This is important since
some tuning of thresholds is usually needed.

The edit script \texttt{tree.hed}, which contains the instructions regarding
which contexts to examine for possible clustering, can be rather long and
complex. A script for automatically generating this file, \texttt{mkclscript},
is found in the RM Demo. A version of the \texttt{tree.hed} script, which can
be used with this tutorial, is included in the \texttt{HTKTutorial} directory.
Note that this script is only capable of creating the TB commands (decision 
tree clustering of states).  The questions (QS) still need defining by
the user.  There is, however, an example list of questions which may be 
suitable to some tasks (or at least useful as an example) supplied with the 
RM demo (lib/quests.hed).  The entire script appropriate for clustering 
English phone models is too long to show here in the text, however, its main 
components are given by the following fragments: