exampsys.tex

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To train a set of HMMs, every file of training data must have an associated
phone level transcription.  Since there is no hand labelled data to bootstrap a
set of models, a flat-start scheme will be used instead.  To do this, two sets
of phone transcriptions will be needed.  The set used initially will have no
short-pause (\texttt{sp}) models between words.  Then once reasonable phone
models have been generated, an \texttt{sp} model will be inserted between words
to take care of any pauses introduced by the speaker.\index{flat start}

The starting point for both sets of phone transcription is an
orthographic\index{transcription!orthographic} transcription in \HTK\ label
format.  This can be created fairly easily using a text editor or a scripting
language.
An example of this is found in the RM Demo at point 0.4. Alternatively, the
script \texttt{prompts2mlf} has been provided in the \texttt{HTKTutorial}
directory.
The effect should be to convert the prompt utterances exampled above into the
following form:
\begin{verbatim}
    #!MLF!#
    "*/S0001.lab"
    ONE 
    VALIDATED 
    ACTS 
    OF 
    SCHOOL 
    DISTRICTS
    .
    "*/S0002.lab"
    TWO 
    OTHER 
    CASES 
    ALSO 
    WERE 
    UNDER 
    ADVISEMENT
    .
    "*/S0003.lab" 
    BOTH 
    FIGURES 
    (etc.)
\end{verbatim}
As can be seen, the prompt labels need to be converted into path names, each
word should be written on a single line and each utterance should be terminated
by a single period on its own.  The first line of the file just identifies the
file as a \textit{Master Label File} (MLF).  This is a single file containing a
complete set of transcriptions.  \HTK\ allows each individual transcription to
be stored in its own file but it is more efficient to use an MLF.
\index{master label files}\index{MLF}

The form of the path name used in the MLF deserves some explanation since it is
really a \textit{pattern} and not a name.\index{master label files!patterns}
When \HTK\ processes speech files, it expects to find a transcription (or 
{\it label file}) with the same name but a different extension.  Thus, if the file
\texttt{/root/sjy/data/S0001.wav} was being processed, \HTK\ would look for a
label file called \texttt{/root/sjy/data/S0001.lab}.  When MLF files are used,
\HTK\ scans the file for a pattern which matches the required label file name.
However, an asterix will match any character string and hence the pattern used
in the example is in effect path independent.  It therefore allows the same
transcriptions to be used with different versions of the speech data to be
stored in different locations.

Once the word level MLF has been created, phone level MLFs can be generated
using the label editor \htool{HLEd}\index{hled@\htool{HLEd}}. For example,
assuming that the above word level MLF is stored in the file
\texttt{words.mlf}, the command
\begin{verbatim}
    HLEd -l '*' -d dict -i phones0.mlf mkphones0.led words.mlf
\end{verbatim}
will generate a phone level transcription of the following form
where the \texttt{-l} option is needed to generate the path '\verb+*+' in the 
output patterns.
\begin{verbatim}
    #!MLF!#
    "*/S0001.lab"
    sil
    w
    ah
    n
    v
    ae
    l
    ih
    d
    .. etc
\end{verbatim}
This process is illustrated in Fig.~\href{f:step4}.

The \htool{HLEd} edit script \texttt{mkphones0.led} 
contains the following commands
\begin{verbatim}
   EX
   IS sil sil
   DE sp
\end{verbatim}
The expand \texttt{EX} command replaces each word in \texttt{words.mlf} 
by the corresponding pronunciation in the dictionary file \texttt{dict}.  
The \texttt{IS}
command inserts a silence model \texttt{sil} at the start and end of
every utterance.  Finally, the delete \texttt{DE} command deletes all
short-pause \texttt{sp} labels, which are not wanted in the transcription
labels at this point.  

\centrefig{step4}{60}{Step 4}

\subsection{Step 5 - Coding the Data}

The final stage of data preparation is to parameterise the raw speech
waveforms into sequences of feature vectors.  \HTK\ support both 
FFT-based\index{analysis!FFT-based}
and LPC-based\index{analysis!LPC-based} analysis.  
Here Mel Frequency Cepstral Coefficients (MFCCs)\index{MFCC coefficients},
which are derived from FFT-based log spectra, will be used.

Coding can be performed using the tool \htool{HCopy}\index{hcopy@\htool{HCopy}} 
configured to\index{coding}
automatically convert its input into MFCC vectors.  To do this, a configuration
file (\texttt{config}) is needed which specifies all of the conversion 
parameters\index{parameterisation}. 
Reasonable settings for these are as follows
\begin{verbatim}
    # Coding parameters
    TARGETKIND = MFCC_0
    TARGETRATE = 100000.0
    SAVECOMPRESSED = T
    SAVEWITHCRC = T
    WINDOWSIZE = 250000.0
    USEHAMMING = T
    PREEMCOEF = 0.97
    NUMCHANS = 26
    CEPLIFTER = 22
    NUMCEPS = 12
    ENORMALISE = F
\end{verbatim}
Some of these settings are in fact the default setting, but they
are given explicitly here for completeness.  In brief, they specify
that the target parameters are to be MFCC using $C_0$ as the energy
component, the frame period is 10msec (\HTK\ uses units of 100ns),
the output should be saved in compressed format, and a crc checksum should
be added.  The FFT should use a Hamming window and the signal should
have first order preemphasis applied using a coefficient of 0.97.
The filterbank should have 26 channels and 12 MFCC coefficients should
be output. 
The variable \texttt{ENORMALISE} is by default true and performs energy
normalisation on recorded audio files. It cannot be used with live audio and
since the target system is for live audio, this variable should be set to
false.

Note that explicitly creating coded data files is not necessary, as coding can
be done "on-the-fly" from the original waveform files by specifying the
appropriate configuration file (as above) with the relevant HTK tools. However,
creating these files reduces the amount of preprocessing required during
training, which itself can be a time-consuming process.

To run \htool{HCopy},  a list of
each source file and its corresponding output file is needed.  For example,
the first few lines might look like\index{extensions!mfc@\texttt{mfc}}
\begin{verbatim}
    /root/sjy/waves/S0001.wav /root/sjy/train/S0001.mfc
    /root/sjy/waves/S0002.wav /root/sjy/train/S0002.mfc
    /root/sjy/waves/S0003.wav /root/sjy/train/S0003.mfc
    /root/sjy/waves/S0004.wav /root/sjy/train/S0004.mfc
    (etc.)
\end{verbatim}
Files containing lists of files are referred to as script files\footnote{
Not to be confused with files containing \textit{edit} scripts
}
and\index{extensions!scp@\texttt{scp}}
by convention are given the extension \texttt{scp} (although 
\HTK\ does not demand this).  Script files are specified using the standard
\texttt{-S} option and their contents are read simply as extensions
to the command line.  Thus, they avoid the need for command lines with
several thousand arguments\footnote{
Most UNIX shells, especially the C shell, only allow a limited and
quite small number of arguments.}.
\index{command line!arguments}\index{command line!script files}

\centrefig{step5}{100}{Step 5}

\noindent
Assuming that the above script is stored in the file \texttt{codetr.scp},
the training data would be coded by executing
\begin{verbatim}
    HCopy -T 1 -C config -S codetr.scp
\end{verbatim}
This is illustrated in Fig.~\href{f:step5}. A similar procedure is
used to code the test data (using \verb|TARGETKIND = MFCC_0_D_A| in
config) after which all of the pieces are in place to start training
the HMMs.
 

\mysect{Creating Monophone HMMs}{egcreatmono}

In this section, the creation of a well-trained set of single-Gaussian
monophone HMMs will be described.  The starting point will be
a set of identical monophone HMMs in which every mean and variance is
identical.  These are then retrained, short-pause models are
added and the silence model is extended slightly.  The monophones
are then retrained.

Some of the dictionary entries have multiple pronunciations.  However,
when \htool{HLEd} was used to expand the word level MLF to create the
phone level MLFs, it arbitrarily selected the first pronunciation it found.
Once reasonable monophone HMMs have been created, the recogniser tool
\htool{HVite} can be used to perform a \textit{forced alignment} 
of\index{forced alignment}
the training data.  By this means, a new phone level MLF is created in which
the choice of pronunciations depends on the acoustic evidence.  This new
MLF can be used to perform a final re-estimation of the monophone HMMs.
\index{monophone HMM!construction of}

\subsection{Step 6 - Creating Flat Start Monophones}

The first step in HMM training is to define a prototype model.  The
parameters of this model are not important, its purpose is to
define the model topology.  For phone-based systems,  a good
topology to use is 3-state left-right with no skips such as the following
\begin{verbatim}
    ~o <VecSize> 39 <MFCC_0_D_A>
    ~h "proto"
    <BeginHMM>
     <NumStates> 5
     <State> 2
        <Mean> 39
          0.0 0.0 0.0 ...
        <Variance> 39
          1.0 1.0 1.0 ...
     <State> 3
        <Mean> 39
          0.0 0.0 0.0 ...
        <Variance> 39
          1.0 1.0 1.0 ...
     <State> 4
        <Mean> 39
          0.0 0.0 0.0 ...
        <Variance> 39
          1.0 1.0 1.0 ...
     <TransP> 5
      0.0 1.0 0.0 0.0 0.0
      0.0 0.6 0.4 0.0 0.0
      0.0 0.0 0.6 0.4 0.0
      0.0 0.0 0.0 0.7 0.3
      0.0 0.0 0.0 0.0 0.0
    <EndHMM>
\end{verbatim}
where each ellipsed vector is of length 39.  This number, 39, is computed from
the length of the parameterised static vector (\texttt{MFCC\_0} = 13) plus
the delta coefficients (+13) plus the acceleration coefficients (+13).

The \HTK\ tool \htool{HCompV}\index{hcompv@\htool{HCompV}} will scan a set of data files, compute
the global mean and variance and set all of the Gaussians in a given HMM
to have the same mean and variance.\index{flat start}
Hence, assuming that a list of all the training files is stored in
\texttt{train.scp}, the command
\begin{verbatim}
    HCompV -C config -f 0.01 -m -S train.scp -M hmm0 proto
\end{verbatim}
will create a new version of \texttt{proto} in the directory \texttt{hmm0}
in which the zero means and unit variances above have been replaced
by the global speech means and variances.
Note that the prototype HMM defines the parameter kind as \texttt{MFCC\_0\_D\_A} (Note: 'zero' not 'oh').
This means that delta and acceleration coefficients are to be computed and
appended to the static MFCC coefficients computed and stored during the
coding process described above.  To ensure that these are computed during loading,
the configuration file \texttt{config} should be modified
to change the target kind, i.e.\ the configuration file entry for
\texttt{TARGETKIND} should be changed to
\begin{verbatim}
   TARGETKIND = MFCC_0_D_A
\end{verbatim}
\htool{HCompV} has a number of options specified for it.  The 
\texttt{-f} option causes a variance floor 
macro\index{variance floor macros} (called \texttt{vFloors}) to be generated which
is equal to 0.01 times the global variance.  This is a vector
of values which will be used to set a floor on the variances estimated
in the subsequent steps.  The \texttt{-m} option asks for means to be computed
as well as variances.  Given this
new prototype model stored in the directory
\texttt{hmm0}, a \textit{Master Macro File}\index{master macro files} 
(MMF) called \texttt{hmmdefs} \index{MMF}
containing a copy for each of the required monophone HMMs is constructed 
by manually copying the prototype and relabeling it for each required 
monophone (including ``sil'').  
The format of an MMF is similar to that
of an MLF and it serves a similar purpose in that it avoids having
a large number of individual HMM definition files\index{HMM!definition files} 
(see Fig.~\href{f:MMFeg}).

\centrefig{MMFeg}{85}{Form of Master Macro Files}

The flat start monophones stored in the directory \texttt{hmm0} are
re-estimated using the embedded re-estimation\index{embedded re-estimation} 
tool \htool{HERest}\index{herest@\htool{HERest}}
invoked as follows
\begin{verbatim}
   HERest -C config -I phones0.mlf -t 250.0 150.0 1000.0 \
    -S train.scp -H hmm0/macros -H hmm0/hmmdefs -M hmm1 monophones0
\end{verbatim}
The effect of this is to load all the models in \texttt{hmm0} which are
listed in
the model list \texttt{monophones0} (\texttt{monophones1} less the short 
pause (\texttt{sp}) model). These are then re-estimated them using the data
listed in \texttt{train.scp} and the new model set is stored in the
directory \texttt{hmm1}.
Most of the files used in this invocation of \htool{HERest} have 
already been described.  The exception is the file \texttt{macros}.
This should contain a so-called \textit{global options} macro and
the variance floor macro \texttt{vFloors} generated earlier.  The global options macro
simply defines the HMM parameter kind and the vector size i.e.
\begin{verbatim}
   ~o <MFCC_0_D_A> <VecSize> 39
\end{verbatim}
See Fig.~\href{f:MMFeg}. This can be combined with \texttt{vFloors} into a text file
called \texttt{macros}.