exampsys.tex

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\begin{verbatim}

    RO 100.0 stats
    TR 0
    QS "L_Class-Stop" {p-*,b-*,t-*,d-*,k-*,g-*} 
    QS "R_Class-Stop" {*+p,*+b,*+t,*+d,*+k,*+g} 
    QS "L_Nasal" {m-*,n-*,ng-*} 
    QS "R_Nasal" {*+m,*+n,*+ng}
    QS "L_Glide" {y-*,w-*} 
    QS "R_Glide" {*+y,*+w}
    ....
    QS "L_w" {w-*} 
    QS "R_w" {*+w} 
    QS "L_y" {y-*} 
    QS "R_y" {*+y} 
    QS "L_z" {z-*} 
    QS "R_z" {*+z} 
 
    TR 2

    TB 350.0 "aa_s2" {(aa, *-aa, *-aa+*, aa+*).state[2]}
    TB 350.0 "ae_s2" {(ae, *-ae, *-ae+*, ae+*).state[2]}
    TB 350.0 "ah_s2" {(ah, *-ah, *-ah+*, ah+*).state[2]}
    TB 350.0 "uh_s2" {(uh, *-uh, *-uh+*, uh+*).state[2]}
    ....
    TB 350.0 "y_s4" {(y, *-y, *-y+*, y+*).state[4]}
    TB 350.0 "z_s4" {(z, *-z, *-z+*, z+*).state[4]}
    TB 350.0 "zh_s4" {(zh, *-zh, *-zh+*, zh+*).state[4]}

    TR 1
    
    AU "fulllist"
    CO "tiedlist"

    ST "trees"
\end{verbatim}
Firstly, the \texttt{RO}\index{ro@\texttt{RO} command} command is used to set
the outlier threshold\index{outlier threshold} to 100.0 and load the statistics
file\index{statistics file} generated at the end of the previous step.  The
outlier threshold determines the minimum occupancy\index{minimum occupancy} of
any cluster and prevents a single outlier state forming a singleton cluster
just because it is acoustically very different to all the other states.  The
\texttt{TR}\index{tr@\texttt{TR} command} command sets the trace level to zero
in preparation for loading in the questions.  Each
\texttt{QS}\index{qs@\texttt{QS} command} command loads a single question and
each question is defined by a set of contexts.  For example, the first
\texttt{QS} command defines a question called \texttt{L\_Class-Stop} which is
true if the left context is either of the stops \texttt{p},
\texttt{b}, \texttt{t}, \texttt{d}, \texttt{k} or \texttt{g}.

\sidefig{step10}{50}{Step 10}{-4}{}
Notice that for a triphone system, it is necessary to include questions
referring to both the right and left contexts of a phone. The questions should
progress from wide, general classifications (such as consonant, vowel, nasal,
diphthong, etc.) to specific instances of each phone.
Ideally, the full set of questions loaded using the \texttt{QS} command would
include every possible context which can influence the acoustic realisation of
a phone, and can include any linguistic or phonetic classification which may be
relevant. There is no harm in creating extra unnecessary questions, because
those which are determined to be irrelevant to the data will be ignored.

The second \texttt{TR} command enables intermediate level progress reporting so
that each of the following \texttt{TB} commands\index{tb@\texttt{TB} command}
can\index{tree building} be monitored.  Each of these \texttt{TB} commands
clusters one specific set of states.  For example, the first \texttt{TB}
command applies to the first emitting state of all context-dependent models for
the phone \texttt{aa}.

Each \texttt{TB} command works as follows.  Firstly, each set of states defined
by the final argument is pooled to form a single cluster.  Each question in the
question set loaded by the \texttt{QS} commands is used to split the pool into
two sets.  The use of two sets rather than one, allows the log likelihood of
the training data to be increased and the question which maximises this
increase is selected for the first branch of the tree. The process is then
repeated until the increase in log likelihood achievable by any question at any
node is less than the threshold specified by the first argument (350.0 in this
case).

Note that the values given in the \texttt{RO} and \texttt{TB} commands affect
the degree of tying and therefore the number of states output in the clustered
system.  The values should be varied according to the amount of training data
available.
As a final step to the clustering, any pair of clusters which can be merged
\index{cluster merging} such that the decrease in log likelihood is below
the threshold is merged.  On completion, the states in each cluster $i$ are
tied to form a single shared state with macro name \texttt{xxx\_i} where
\texttt{xxx} is the name given by the second argument of the \texttt{TB}
command.

The set of triphones used so far only includes those needed to cover the
training data. The \texttt{AU} command takes as its argument a new list of
triphones expanded to include all those needed for recognition.  This list can
be generated, for example, by using \htool{HDMan} on the entire dictionary (not
just the training dictionary), converting it to triphones using the command
\texttt{TC} and outputting a list of the distinct triphones to a file using the
option \texttt{-n} 

\begin{verbatim}
    HDMan -b sp -n fulllist -g global.ded -l flog beep-tri beep
\end{verbatim}

\noindent
The -b sp option specifies that the sp phone is used as a word boundary, and so 
is excluded from triphones.  The effect of the \texttt{AU} command is to use the 
decision trees to synthesise all of the new previously unseen triphones in the new 
list.
\index{au@\texttt{AU} command}

Once all state-tying has been completed and new models synthesised, 
some models may  share exactly
the same 3 states and transition matrices and are thus identical.
The \texttt{CO} command\index{co@\texttt{CO} command}\index{model compaction} is used
to compact the model set by finding all identical models and tying them
together\footnote{
Note that if the transition matrices had not been tied, the \texttt{CO}
command would be ineffective since all models would be different by
virtue of their unique transition matrices.}, producing a new list of models
called \texttt{tiedlist}.

One of the advantages of using decision tree clustering is that it allows
previously\index{unseen triphones}
unseen triphones to be synthesised.  To do this, the trees must
be saved and this is done by the \texttt{ST} command\index{st@\texttt{ST} command}.
Later if new previously unseen triphones are required, for example in the
pronunciation of a new vocabulary item, the existing model set can be
reloaded into \htool{HHEd}, the trees reloaded using 
the \texttt{LT} command\index{lt@\texttt{LT} command}
and then a new extended list of triphones created using 
the \texttt{AU} command.\index{au@\texttt{AU} command}

After \htool{HHEd} has completed,  the effect of tying can be studied and
the thresholds adjusted if necessary.  The log file will
include summary statistics which give the total number of physical
states remaining and the number of models after compacting.

Finally, and for the last time, the models are re-estimated twice using
\htool{HERest}.  Fig.~\href{f:step10} illustrates this last step in the HMM
build process.  The trained models are then contained in the file
\texttt{hmm15/hmmdefs}.

\mysect{Recogniser Evaluation}{egrectest}

The recogniser is now complete and its performance can be evaluated.  
The recognition network and dictionary have already been constructed, 
and test data has been recorded.  
Thus, all that is necessary is to run the recogniser and 
then evaluate the results using the \HTK\ analysis tool \htool{HResults}\index{recogniser evaluation}

\subsection{Step 11 - Recognising the Test Data}

Assuming that \texttt{test.scp} holds a list of the coded test files,
then each test file will be recognised and its transcription output to
an MLF called \texttt{recout.mlf} by executing the following
\begin{verbatim}
    HVite -H hmm15/macros -H hmm15/hmmdefs -S test.scp \
          -l '*' -i recout.mlf -w wdnet \
          -p 0.0 -s 5.0 dict tiedlist
\end{verbatim}
The options \texttt{-p} and \texttt{-s} set the \textit{word insertion penalty}
\index{word insertion penalty}
and the \textit{grammar scale factor}, \index{grammar scale factor}
respectively.  The word insertion penalty
is a fixed value added to each token when it transits from the end of one word
to the start of the next.  The grammar scale factor is the amount by which
the language model probability is scaled before being 
added to each token  as it transits from the end of one word
to the start of the next.  These parameters can have a significant effect
on recognition performance and hence, some tuning on development test data
is well worthwhile.

The dictionary contains monophone transcriptions whereas the supplied HMM list
contains word internal triphones.  \htool{HVite}\index{hvite@\htool{HVite}} 
will make the necessary 
conversions when loading the word network \texttt{wdnet}.  However, 
if the HMM list contained both monophones and context-dependent phones
then \htool{HVite} would become confused.  The required form of 
word-internal network\index{networks!word-internal} 
expansion can be forced by setting the configuration variable
\texttt{FORCECXTEXP}\index{forcecxtexp@\texttt{FORCECXTEXP}} to true and 
\texttt{ALLOWXWRDEXP}\index{allowxwrdexp@\texttt{ALLOWXWRDEXP}} to 
false (see chapter~\ref{c:netdict} for details).\index{accuracy figure}

Assuming that the MLF \texttt{testref.mlf} contains word level transcriptions
for each test file\footnote{The \htool{HLEd} tool may have to be used to insert silences 
at the start and end of each transcription or alternatively
\htool{HResults} can be used to ignore silences (or any other symbols) using
the \texttt{-e} option}, the actual
performance can be determined by running 
\htool{HResults} as follows
\begin{verbatim}
    HResults -I testref.mlf tiedlist recout.mlf
\end{verbatim}
the result would be a print-out of the form
\begin{verbatim}
    ====================== HTK Results Analysis ==============
      Date: Sun Oct 22 16:14:45 1995
      Ref : testrefs.mlf
      Rec : recout.mlf
    ------------------------ Overall Results -----------------
    SENT: %Correct=98.50 [H=197, S=3, N=200]
    WORD: %Corr=99.77, Acc=99.65 [H=853, D=1, S=1, I=1, N=855]
    ==========================================================
\end{verbatim}
The line starting with \texttt{SENT:} indicates that of the 200 test utterances,
197  (98.50\%) were correctly recognised.  The following line starting with \texttt{WORD:} 
gives the word level statistics and indicates that of the 855 words in total,
853 (99.77\%) were recognised correctly.  There was 1 deletion error (\texttt{D}), 
1 substitution\index{recognition!results analysis}
error (\texttt{S}) and 1 insertion error (\texttt{I}).  The accuracy figure (\texttt{Acc})
of 99.65\% is lower than the percentage correct (\texttt{Cor}) because it takes
account of the insertion errors which the latter ignores.

\centrefig{step11}{120}{Step 11}

\mysect{Running the Recogniser Live}{egreclive}

The recogniser can also be run with live input\index{live input}.  
\index{recognition!direct audio input}
To do this it is only
necessary to set the configuration variables needed to convert the input
audio to the correct form of  parameterisation.  Specifically, the following
needs to be appended to the configuration file \texttt{config} to
create a new configuration file \texttt{config2}
\begin{verbatim}
    # Waveform capture
    SOURCERATE=625.0
    SOURCEKIND=HAUDIO
    SOURCEFORMAT=HTK
    ENORMALISE=F
    USESILDET=T
    MEASURESIL=F
    OUTSILWARN=T
\end{verbatim}
These indicate that the source is direct audio with sample period 62.5
$\mu$secs.  The silence detector is enabled and a measurement of the background
speech/silence levels should be made at start-up.  The final line makes sure
that a warning is printed when this silence measurement is being made.

Once the configuration file has been set-up for direct audio input,
\htool{HVite} can be run as in the previous step except that no files need be
given as arguments

\begin{verbatim}
    HVite -H hmm15/macros -H hmm15/hmmdefs -C config2 \
          -w wdnet -p 0.0 -s 5.0 dict tiedlist
\end{verbatim}

On start-up, \htool{HVite} will prompt the user to speak an
arbitrary sentence (approx. 4 secs) in order to measure the speech and
background silence levels. It will then repeatedly recognise and, if trace
level bit 1 is set, it will output each utterance to the terminal. A typical
session is as follows\index{recognition!output}

\begin{verbatim}
   Read 1648 physical / 4131 logical HMMs
   Read lattice with 26 nodes / 52 arcs
   Created network with 123 nodes / 151 links

   READY[1]>
   Please speak sentence - measuring levels
   Level measurement completed
   DIAL FOUR SIX FOUR TWO FOUR OH  
        == [303 frames] -95.5773 [Ac=-28630.2 LM=-329.8] (Act=21.8)
   
   READY[2]>
    DIAL ZERO EIGHT SIX TWO 
        == [228 frames] -99.3758 [Ac=-22402.2 LM=-255.5] (Act=21.8)
   
   READY[3]>
    etc
\end{verbatim}
During loading, information will be printed out regarding the different
recogniser components. The physical models are the distinct HMMs used by 
the system, while the logical models include all model names. The number 
of logical models is higher than the number of physical models because many 
logically distinct models have been determined to be physically identical 
and have been merged during the previous model building steps. The lattice
information refers to the number of links and nodes in the recognition syntax.
The network information refers to actual recognition network built by
expanding the lattice using the current HMM set, dictionary and any context
expansion rules specified.
After each utterance, the numerical information gives the total number
of frames, the average log likelihood per frame, the total acoustic score,
the total language model score and the average number of models active.

Note that if it was required to recognise a new name, then the
following two changes would be needed
\begin{enumerate}
\item the grammar would be altered to include the new name
\item a pronunciation for the new name would be added to the dictionary
\end{enumerate}
If the new name required triphones which did not exist, then they could be
created by loading the existing triphone set into
\htool{HHEd}\index{hhed@\htool{HHEd}}, loading the decision trees using the
\texttt{LT} command\index{lt@\texttt{LT} command} and then using the
\texttt{AU} command\index{au@\texttt{AU} command} to generate a new complete
triphone set.\index{triphones!synthesising unseen}

\mysect{Summary}{exsyssum}
This chapter has described the construction of a tied-state phone-based
continuous speech recogniser and in so doing, it has touched on most of the
main areas addressed by \HTK: recording, data preparation, HMM definitions,
training tools, adaptation tools, networks, decoding and evaluating.  The 
rest of this book discusses each of these topics in detail.



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