Helmer Strik, Catia Cucchiarini
Dept. of Language & Speech, University of Nijmegen
P.O. Box 9103, 6500 HD Nijmegen, The Netherlands
In: H. Strik, J.M. Kessens, M. Wester (eds.),
Proc. of the ESCA workshop 'Modeling Pronunciation Variation for Automatic Speech Recognition', Rolduc,
Kerkrade, 4-6 May 1998, pp. 137-144.
In this contribution an overview is provided of the papers
presented at this workshop. First, the most important
characteristics that distinguish the various studies on
pronunciation variation modeling are discussed. Subsequently,
the issues of evaluation and comparison are addressed.
Particular attention is paid to some of the most important
factors that make it difficult to compare the different methods
in an objective way. Finally, some conclusions are drawn as to
the importance of objective evaluation and the way in which it
could be carried out.
If words were always pronounced in the same way, automatic
speech recognition (ASR) would be relatively easy. However, for
various reasons words are almost always pronounced differently.
This variation in pronunciation is a major problem in ASR.
In the beginning of ASR research the amount of pronunciation
variation was limited by using isolated words. In isolated word
recognition the speakers have to pause between words. In
general, the consequence is that they also articulate more
carefully. Although using isolated words makes the task of an
ASR system easier, it certainly does not do the same for the
speaker. On the contrary, pausing between words is highly
unnatural. Therefore, attempts were made in ASR research to
improve technology so that it could handle less artificial
speech. As a consequence, the type of speech used in ASR
research has gradually progressed from isolated words to
connected words, carefully read speech, and finally
conversational or spontaneous speech. Although many current
applications still make use of isolated word recognition (e.g.
dictation), in ASR research the emphasis is now on spontaneous
or conversational speech.
It is clear that in going from isolated words to
conversational speech the amount of pronunciation variation
increases. Since the presence of variation in pronunciation may
cause errors in ASR, modeling pronunciation variation is seen
as a possible way of improving the performance of the current
systems. As a matter of fact, there has been an increase in the
amount of research on this topic (see e.g. ), which is
evident from the growing number of contributions to
conferences, and also from the organization of this workshop.
The aim we had in mind when we decided to organize this
meeting was to create the opportunity for researchers working
on this topic to have in-depth discussions on the problem of
pronunciation variation and its possible solutions. Moreover,
we thought it would be very interesting if, on the basis of
these discussions, it were possible to draw some conclusions as
to the best way in which to approach the pronunciation
variation modeling problem. This would require an objective
comparison of the methods proposed by the various authors.
To pave the way for this kind of discussion at the workshop,
this paper provides an overview of the methods that will be
presented at this meeting. The presentation of the various
methods will be organized around some of the major
characteristics that distinguish pronunciation variation
modeling techniques from each other. In illustrating these
characteristics we will not limit ourselves to the
contributions to this workshop, but, where necessary, reference
will be made to related research that has been presented
After having presented the different techniques, we will
address the issues of evaluation and comparison, which are
crucial if we want to draw conclusions as to the merits of the
various proposals. In particular, we will discuss the most
important factors that make it difficult to compare the
different methods in an objective way.
2. CHARACTERISTICS OF THE METHODS
In choosing which method to use for pronunciation variation
modeling a number of decisions have to be made. These decisions
concern the following questions:
- Which type of pronunciation variation should be modeled?
- Where should the information on variation come from?
- Should the information be formalized or not?
- In which component of the automatic speech recognizer
should variation be modeled?
It is obvious that these questions cannot be answered in
isolation. On the contrary, the answers will be highly
interdependent. Depending on the decision taken for each of the
above questions, different methods for pronunciation variation
modeling can be distinguished. Below we will consider these
questions and the possible answers in more detail.
2.1. Type of pronunciation variation
With respect to the type of pronunciation variation to be
modeled the choice is between variation within words and
variation across word boundaries. In general, this choice will
be influenced by several factors such as the type of ASR and
the language which is used, and the level at which modeling
will take place.
Modeling within-word variation is an obvious choice if the
ASR makes use of a lexicon with word entries, because in this
case variants can simply be added to the lexicon. Given that
almost all ASRs use such a lexicon, within-word variation is
modeled in the majority of the methods [1, 2, 3, 4, 5, 6, 7, 8,
10, 11, 12, 13, 17, 20, 22, 23, 24, 25, 33, 36]. However, there
are methods that model cross-word variation such as [5, 16, 22,
26, 27, 28].
A sort of compromise solution between the ease of modeling at
the level of the lexicon and the need to model cross-word
variation is to use multi-words [3, 15, 19, 24, 39]. In this
approach sequences of words (usually called multi-words) are
treated as one entity in the lexicon (see section 2.4.1.).
2.2. Information sources
Once decisions have been made as to the type of variation, it
is important to choose the source from which information on
pronunciation variation will be retrieved. In this regard a
distinction can be drawn between data-driven vs. knowledge-based methods.
In data-driven methods the information on pronunciation
variation is mainly obtained from the acoustic signals [2, 5,
7, 8, 9, 10, 11, 13, 17, 18, 19, 20, 25, 27, 28, 33]. In this
type of approach the acoustic signals are usually first
transcribed. Subsequently the transcriptions can be used for
different purposes, as will be explained in section 2.3.
Transcriptions of the acoustic signals can be obtained either
manually [7, 9, 10, 19, 20, 26, 27, 31] or automatically [2, 5,
8, 10, 12, 13, 15, 19, 25, 28, 33]. Given that acquiring manual
transcriptions for very large corpora is extremely time-consuming, and therefore costly, the use of automatically
obtained transcriptions is becoming more common. Moreover,
there is another reason why transcriptions obtained
automatically with the ASR itself could be beneficial, viz.
that these transcriptions are more in line with the phone
strings obtained later during recognition with the same ASR.
This is also mentioned by Riley et al.  who conclude:
"Further, our results indicate that while a handlabeled corpus
is very useful as a bootstrapping device, estimates of
pronunciation probabilities, context effects, etc., are best
derived from larger amounts of automatic transcriptions,
preferably done using the same set of acoustic models which
will eventually be used for recognition."
In knowledge-based studies information on pronunciation
variation is primarily derived from sources that are already
available [1, 4, 6, 12, 14, 16, 21, 22, 23, 24, 26, 31, 36, 37,
38, 39, 42]. In general, the way in which the information on
pronunciation variation is represented varies for the different
sources. It can be a formalized representation in terms of
rules, as in linguistic studies, or enumerated information in
terms of pronunciation forms, as in pronunciation dictionaries.
These two types of representations are discussed in detail in
The distinction between the data-driven and the knowledge-based approaches is related to the difference between bottom-up
and top-down, which are also commonly used terms in ASR
literature. However, in this paper these terms will not be used
interchangeably. More explicitly, the terms data-driven and
knowledge-based are taken to refer to the starting point of the
research, be it the acoustic signals (data) or the literature
(knowledge). On the other hand, the terms bottom-up and top-down refer to the direction of the developing process, which
can be upward or downward.
In this sense most studies presented at this workshop can be
said to be either data-driven or knowledge-based, because for
each of them it is possible to say what the starting point of
the research was (see the references above). However, most of
them cannot be said to be completely bottom-up or top-down,
because in none of these studies the direction of the
developing process is solely upward or downward, the flow of
information can be in both directions. For example, in many
data-driven studies the results of the bottom-up analyses are
used to change the lexicon and the altered lexicon is then used
during recognition in a top-down manner. Similarly, knowledge-based methods are usually not strictly top-down, because, for
example, in many of them the rules applied to generate
pronunciation variants may be altered on the basis of
information derived from analysis of the acoustic signals.
In general terms it is not possible to say whether a data-driven study is to be preferred to a knowledge-based one. A
possible drawback of knowledge-based studies is that there
could be a mismatch between the information found in the
literature and the data for which it has to be used. In the
introduction it was stated that in ASR research the emphasis is
now on spontaneous speech. However, the knowledge on
pronunciation variation that can be found in the literature
usually concerns other speech styles. Therefore, it is possible
that the information obtained from the literature does not
cover the type of variation in question, whereas information
obtained from data could be more effective for this purpose. To
overcome this problem one can resort to a combination of top-down and bottom-up approaches, as explained above.
On the other hand, a possible disadvantage of data-driven
studies is that for every new corpus the whole process of
transcribing the speech material and deriving information on
pronunciation variation has to be repeated. In other words,
information obtained on the basis of data-driven studies does
not generalize easily to situations other than the one in
2.3. Information representation
Regardless of whether a data-driven or a knowledge-based
approach is used, it is possible to choose between formalizing
the information on pronunciation variation or not. In general,
formalization means that a more abstract and compact
representation is chosen, e.g. rewrite rules or artificial
In a data-driven method the formalizations are derived from
the data [5, 8, 27, 28, 30, 33, 41]. In general this is done in
the following manner. The bottom-up transcription of an
utterance is aligned with its corresponding top-down
transcription obtained by concatenating the transcriptions of
the individual words contained in the lexicon. Alignment is
done by means of a Dynamic Programming (DP) algorithm [5, 7, 8,
10, 26, 27, 28, 33, 41]. The resulting DP-alignments can then
be used to
- derive rewrite rules [5, 27, 28]
- train an artificial neural network (ANN) [8, 30, 33]
- calculate a phone confusion matrix .
In these three cases the information about pronunciation
variation present in the DP-alignments is formalized in terms
of rewrite rules, ANNs and a phone confusion matrix,
In a knowledge-based approach formalized information on
pronunciation variation can be obtained from linguistic studies
in which rules have been formulated. In general these are
optional phonological rules concerning deletions, insertions
and substitutions of phones [1, 6, 12, 15, 16, 22, 23, 24, 26,
39]. Rules (either obtained from data or from linguistic
studies) and ANNs are then used to generate the various
The obvious alternative to using formalizations is to use
information that is not formalized, but enumerated. Again, this
can be done either in a data-driven or in a knowledge-based
manner. In data-driven studies the bottom-up transcriptions can
be used to list all pronunciation variants of one and the same
word. These variants and their transcriptions can then be added
to the lexicon. Alternatively, in knowledge-based studies it is
possible to add all the variants of one and the same word
contained in a pronunciation dictionary. Quite clearly, when no
formalization is used, it is not necessary to generate the
variants because they are already available.
It is not easy to decide a priori whether formalized
information will work better than enumerated information. It
may at first seem that using formalizations has two important
advantages. First, one has complete control over the process of
variant generation. At any moment it is possible to select
variants automatically in different ways. Second, since the
information on pronunciation variation is expressed in more
abstract terms, it follows that it is not limited to a specific
corpus and that it can easily be applied to other corpora. Both
these operations will be less easy with enumerated information.
However, the use of formalizations also has some disadvantages,
like overgeneration and undergeneration, owing to incorrect
specifications of the rules applied, or overcoverage and
undercoverage. Both types of problems should not arise when
using enumerated information.
2.4. Level of modeling
Given that most ASRs consist of three components, there are
three levels at which variation can be modeled: the lexicon,
the acoustic models, and the language model. This is not to say
that modeling at one level precludes modeling at one of the
other levels, on the contrary. For example, variation modeling
can happen in the lexicon and in the language model
simultaneously, as will be described below.
At the level of the lexicon, pronunciation variation is usually
modeled by adding pronunciation variants (and their
transcriptions) to the lexicon [1, 3, 4, 5, 6, 8, 11, 12, 13,
15, 19, 21, 24, 25, 26, 27, 28, 31, 36, 41]. The rationale
behind adding pronunciation variants to the lexicon is that
with multiple transcriptions of the same word the chance is
increased that for an incoming signal the speech recognizer
selects a transcription belonging to the correct word. In turn,
this should lead to lower error rates.
However, adding pronunciation variants to the lexicon usually
also introduces new errors because the acoustic confusability
within the lexicon increases, i.e. the transcriptions of the
added variants can be confused with those of other entries in
the lexicon. This can be minimized by making an appropriate
selection of the pronunciation variants, by, for instance,
adding only the set of variants for which the balance between
solving old errors and introducing new ones is positive.
Therefore, in many studies tests are carried out to determine
which set of pronunciation variants leads to the largest gain
in performance of the ASR [5, 8, 11, 12, 13, 15, 19, 24, 27,
28, 36, 41]. For this purpose different criteria can be used,
such as frequency of occurrence of the variants [24, 36],
degree of confusability between the variants  or a maximum
likelihood criterion .
As was mentioned earlier, multi-words can also be added to
the lexicon, in an attempt to model cross-word variation at the
level of the lexicon. Optionally, the pronunciation variants of
multi-words could also be included in the lexicon. By using
multi-words Beulen et al.  and Wester et al.  achieve a
substantial improvement. On the other hand, Nock and Young 
conclude that "No clear evidence of multi-words being
beneficial was found under any of the selection criteria".
Before variants can be selected, they have to be obtained, in
the first place. In the previous section we saw that variants
can either be obtained directly (from data or available
sources), or be generated by means of rewrite rules or ANNs.
The contributions to this workshop contain examples of all
Since rule-based methods are probably the methods used most
often, it is interesting to note that Nock & Young 
conclude that "rule-based learning methods may not be the most
appropriate for learning pronunciations when starting from a
carefully constructed, multiple pronunciation dictionary". The
question here is whether this conclusion is also valid for
other applications in other languages, and whether it is
possible to decide in which cases the starting point is a
carefully constructed, multiple pronunciation dictionary (see
also section 3.).
In  the two types of methods for obtaining variants,
rule-based and enumerated, are compared. The baseline system
makes use of a canonical lexicon with 194 words. If the
variants generated by rule are added to the canonical lexicon,
making a total of 291 entries, a substantial improvement is
observed. However, if all variants observed in the
transcriptions of a corpus are added to the canonical lexicon,
making a total of 897 entries, an even larger improvement is
found. In this particular example adding all variants found in
the corpus would seem to produce better results than adding a
smaller number of variants generated by rule. In this respect
some comment is in order.
First, in this example the number of entries in the lexicon
was small. It is not clear whether similar results would be
obtained with larger lexica. One could imagine that
confusability does not increase linearly, and with many entries
and many variants it could lead to less positive results.
Second, the fact that a method in which variants are taken
directly from transcriptions of the acoustic signals works
better than a rule-based one could also be due to the
particular nature of the rules in question. As was pointed out
in section 2.2., rules taken from the literature are not always
the optimal ones to model variation in spontaneous speech,
while information obtained from data may be much better suited
for this purpose.
2.4.2. Acoustic models
Pronunciation variation can also be represented at the level of
the acoustic models, for instance by optimizing the acoustic
models [2, 3, 4, 9, 10, 15, 19, 23, 24, 29, 34, 35, 36].
Optimization can be attained in different ways.
18.104.22.168. Iterative transcribing
An obvious way of optimizing the acoustic models is by using a
procedure which we will refer to as iterative transcribing. In
this procedure pronunciation variants are used both during
training and recognition [3, 19, 23, 24, 36]. The goal of this
procedure is the alternate improvement of the transcriptions
contained in the training corpus and of the acoustic models
trained on this corpus. The transcriptions available and a
canonical lexicon are the starting points. These are used to
train the first set of acoustic models. Subsequently, the
pronunciation variants are added to the lexicon. For every word
in the corpus for which pronunciation variants are present in
the lexicon, the ASR itself selects the optimal one. In this
way new, updated transcriptions are obtained which, in turn,
are used to train new acoustic models. Updating the
transcriptions and re-training the acoustic models can be
In general, this procedure seems to improve the performance
of the ASR [19, 23, 24, 36]. However, Beulen et al.  found
that in some cases the performance does not improve, but
remains unchanged or even deteriorates. Furthermore, the
beneficial effect of including pronunciation variants during
recognition is usually larger than that deriving from iterative
transcribing. In spite of this, it seems worthwhile to test
iterative transcribing because it is a relatively
straightforward procedure that can be applied almost completely
automatically, and because it usually gives an improvement over
and above that of using multiple variants during recognition
22.214.171.124. Other basic units
In most ASRs the phone is used as the basic unit and,
consequently, the lexicon contains transcriptions in the form
of strings of phone symbols. However, in some studies
experiments are performed with basic units of recognition other
than the phone.
For this purpose sub-phonemic models have been proposed [29,
34]. In  a set of multi-valued phonological features is
used. First, the feature values of the speech units in
isolation are defined followed by the (often optional)
spreading of features for speech units in context. On the basis
of the resulting feature-overlap pattern a pronunciation
network is created. The starting point in  is a set of
symbols for (allo-)phones and sub-phonemic segments. These
symbols are used to model pronunciation variation due to
context, coarticulation, dialect, speaking style and speaking
rate. The resulting descriptions (in which almost half of the
segments are optional) are used to create pronunciation
networks. In both cases the ASR will decide during decoding
what the optimal path in the pronunciation networks is.
Besides sub-phonemic models it is also possible to use basic
units larger than phones, like e.g. (demi-)syllables [9, 10] or
even whole words. It is clear that using word models is only
feasible for tasks with a limited vocabulary (e.g. digit
recognition). For most tasks the number of words, and thus the
number of word models to be trained, is simply too large.
Therefore, in some cases word models are only trained for the
words occurring most frequently, while for the less frequent
words sub-word models are used. Since the number of syllables
is usually much smaller than the number of words [9, 10], the
syllable would seem to be suited as the basic unit of
recognition. Greenberg  mentions several other reasons why,
given the existing pronunciation variation, the syllable is a
suitable candidate. If syllable models are used, the within-syllable variation can be modeled by the stochastic model for
the syllable, just as the within-phone variation is modeled by
the acoustic model of the phone [see e.g. 10]. For instance, in
phone-based systems deletions, insertions and substitutions of
phones have to be modeled explicitly (e.g. by including
multiple pronunciations in the lexicon), while in a syllable-based system these processes would result in different
realizations of the syllable.
In most ASRs the basic units are defined a priori.
Furthermore, while the acoustic models for these basic units
are calculated with an optimization procedure, the
pronunciations in the lexicon are usually handcrafted. However,
it is also possible to allow an optimization procedure to
decide what the optimal pronunciations in the lexicon and the
optimal basic units (i.e. both their size and the corresponding
acoustic models) are [2, 35]. In both  and  the
optimization is done with a maximum likelihood criterion.
In  no tests are described. For the syllable models in
 the resulting levels of performance are lower than those
of standard ASRs. Furthermore, in [2, 29, 34, 35] the observed
levels of performance are comparable to those of phone-based
ASRs (usually for limited tasks). Although these results are
promising, it remains to be seen whether these methods are more
suitable for modeling pronunciation variation than standard
phone-based ASRs, especially for tasks in which a large amount
of pronunciation variation is present (e.g. for conversational
2.4.3. Language models
Another component in which pronunciation variation can be taken
into account is the language model (LM) [5, 8, 12, 16, 23, 24,
27, 28, 30, 39]. This can be done in several ways as will be
Let X be the speech signal that has to be recognized. The
goal is to find the string of words W that maximizes
P(X|W)*P(W). Usually N-grams are used to calculate P(W). If
there is one entry for every word in the lexicon the N-grams
can be calculated in the standard way. As we have seen above
the most common way to model pronunciation variation is to add
pronunciation variants to the lexicon. The problem then is how
to deal with these pronunciation variants at the level of the
Method 1. The first solution is to simply use the variants
themselves (instead of the underlying words) to calculate the
N-grams [23, 24]. For this procedure a transcribed corpus is
needed which contains information about the realized
pronunciation variants. Such a corpus can be obtained in a
data-driven manner (see section 2.2.) or by the procedure of
iterative transcribing (see section 126.96.36.199.). The goal of this
method is to find the string of variants V which maximizes
Method 2. A second solution would be to introduce an
intermediate level: P(X|V)*P(V|W)*P(W). The goal now is to find
the string of words W and the corresponding string of variants
V that maximizes the latter equation [5, 8, 16, 27, 28, 39].
The unigram determines the probability of a variant given the
word, while the higher-order N-grams (i.e. N > 1) describe the
probabilities of sequences of words. In this case the unigram
probabilities can also be calculated on the basis of a
transcribed corpus. However, they can also be obtained
otherwise. If the pronunciation variants are generated by rule,
the probabilities of these rules can be used to determine the
probabilities of the pronunciation variants [5, 12]. Likewise,
if an ANN is used to generate pronunciation variants, the ANN
itself can produce probabilities of the pronunciation variants
It is obvious that the number of pronunciation variants is
larger than the number of words. As a consequence, more
parameters have to be trained for the first method than for the
second. This could be a disadvantage of the first method, since
sparsity of data is a common problem during the training of
LMs. A way of reducing the number of parameters for both
methods is to use thresholds, i.e. only pronunciation variants
which occur often enough are taken into account.
Another important difference between the two methods is that
in the second method the context-dependence of pronunciation
variants cannot be modeled. This can be a disadvantage as
pronunciation variation is often context-dependent, e.g.
liaison in French [16, 39]. Within the second method this
deficiency can be overcome by using classes of words instead of
the words themselves, i.e. the classes of words that do or do
not allow liaison [16, 39]. The probability of a pronunciation
variant for a certain class is then represented in the unigram,
while the probability of sequences of word classes is stored in
the higher-order N-grams.
3. EVALUATION AND COMPARISON
In the previous section the various methods of modeling
pronunciation variation have been described according to their
major properties. In this presentation the emphasis was on the
various characteristics of the methods, and not so much on
their merits. This is not to say that the effectiveness of a
method is not important. On the contrary, the extent to which
each method achieves the goal it was intended for, be it
reducing the number of errors caused by pronunciation variation
or getting more insight into pronunciation variation, is a
fundamental aspect, especially if we want to draw general
conclusions as to the different ways in which pronunciation
variation in ASR can best be addressed.
Although studies that provide insight into the processes
underlying pronunciation variation are very useful (e.g. [9,
17]), the majority of the papers presented at this workshop
focus on reducing word error rate (WER) by modeling
pronunciation variation. The effectiveness of studies of this
kind is usually established by comparing the performance of the
baseline system (the starting point) with the performance
obtained after the method has been applied. For every
individual study, this seems a plausible procedure. The amounts
of improvement reported in the literature (see e.g. the papers
in this proceedings) differ from almost none (and occasionally
even a deterioration) to substantial ones.
In trying to draw general conclusions as to the effectiveness
of the various methods one is then tempted to conclude that the
method for which the largest improvement was observed is the
best one. In this respect some comment is in order. First, it
is unlikely that there will be one single best approach, as the
tasks of the various systems are very different. Second, we are
not interested in finding a winner, but in gaining more insight
into the way in which pronunciation variation can best be
approached. Third, it is wrong to take the change in WER as the
only criterion for evaluation, because this change is dependent
on at least three different factors: 1. the corpora, 2. the
ASR, and 3. the baseline system. This means that improvements
in WER can be compared with each other only if in the methods
under study these three elements were identical or at least
similar. It is obvious that in the majority of the methods
presented these three elements are not kept constant. On the
contrary, they are usually very different. In the following
sections we discuss these differences and try to explain why
this makes it difficult to compare the various methods and, in
particular, the results obtained with each of them.
3.1. Differences between corpora
Corpora are used to gauge the performance of ASRs. In studies
on pronunciation variation modeling many different corpora are
used. The choice of a given corpus implies at the same time the
choice of the task, the type of speech and the language. This
means that there are at least three respects in which corpora
may differ from each other.
Very often the task or application also dictates the type of
speech that will have to be recognized. Both with respect to
task and type of speech it is possible to distinguish between
cases with little pronunciation variation (carefully read
speech) and cases with much more variation (conversational,
spontaneous speech). Given this difference in amount of
variation, it is possible that a method for pronunciation
variation modeling that performs well for read speech does not
perform equally well for conversational speech.
Another important aspect of the corpus is the language. Since
pronunciation variation will also differ between languages, a
method which gives good results in one language need not be as
effective in another language. For example, Beulen et al. 
report improvements for English corpora while with the same
method no improvements were obtained for a German corpus.
Another example concerns the pronunciation variation caused by
liaison in French. Perennou and Brieussel-Pousse [16, 39]
propose a method to model this type of pronunciation variation,
and for their French corpus this yields an improvement.
However, it remains to be seen how effective their method is in
modeling pronunciation variation in other languages in which
there is less or no liaison.
3.2. Differences between ASRs
As we all know, not all ASRs are similar. A method that works
well for a certain ASR, can be less successful with another
ASR. This will already be the case for ASRs with a similar
architecture (i.e. a standard ASR' with the common phone-based
HMMs), but it will certainly be true for ASRs with totally
different architectures. For instance, Cremelie and Martens 
obtain large improvements with a rule-based method for their
segment-based ASR. However, this does not imply that the same
rule-based method will be equally successful for another type
Moreover, a method can be successful with a given ASR, not so
much because it models pronunciation variation in the correct
way, but because it corrects for the peculiarities of the ASR.
To illustrate this point let us assume that a specific ASR very
often recognizes /n/ in certain contexts as /m/. If the method
for pronunciation variation modeling replaces the proper
occurrences of /n/ by /m/ in the lexicon, the performance will
certainly go up. Such a transformation is likely to occur in a
data-driven method in which a DP-alignment is used (see section
2.3.). By looking at the numbers alone (the performance before
and after the method was applied) one could conclude that the
method is successful. However, in this particular case the
method is successful only because it corrects the errors made
by the ASR. Although one could argue that the error made by the
ASR (i.e. recognizing certain /n/s as /m/) is in fact due to
pronunciation variation, the example clearly demonstrates that
certain methods may work with a specific ASR, but do not
necessarily generalize to other systems.
Let us state clearly that being able to correct for the
peculiarities of an ASR is not a bad property of a method. On
the contrary. If a method has this property it is almost
certain that it will increase the performance of the ASR. This
is probably why in  it is argued that the ASR itself should
be used to make the transcriptions. The point to be made in the
example above is that a posteriori it is not easy to determine
which part of the improvement is due to correct modeling of
pronunciation variation by the method or due to other reasons.
In turn, this will make it difficult to estimate how successful
a method will be for another ASR. After all, the peculiarities
of all ASRs are not the same.
3.3. Differences in the baseline system
Another reason why it is difficult to compare methods is
related to the baseline systems (the starting points) used. In
order to illustrate this point, let us first recall briefly
what a common method of evaluation is in this field of
research. First, the performance is calculated for the baseline
system, say WERbegin. Then the method is applied, e.g. by adding
pronunciation variants to the lexicon, and the performance of
the new system is determined, say WERend. The absolute
improvement then is:
%abs = WERbegin - WERend
This is usually expressed in relative terms:
%rel = (WERbegin - WERend)/WERbegin
The measure %rel yields higher numbers than the measure %abs,
but even higher numbers can be obtained by using
%rel2 = (WERbegin - WERend)/WERend
The last equation is generally considered to be less correct.
Furthermore, for most people %rel is more in agreement with
their intuition than %rel2, i.e. most people would say that an
improvement from 10% to 5% WER is an improvement of 50% and not
an improvement of 100%.
Whatever equation is used, it is clear that the outcome of
the equation depends on two numbers: WERbegin and WERend. In most
studies a lot of work is done in order to decrease WERend, and
this work is generally described in detail. However, more often
than not the baseline system is not clearly described and no
attempt is made to improve it. Usually the starting point is
simply an ASR that was available at the beginning of the
research, or an ASR that is quickly trained with resources
available at the beginning of the research. It is clear that
for a relatively bad baseline system it is much easier to
obtain improvements than for a good baseline system. For
instance, a baseline system may contain errors, like e.g.
errors in the canonical lexicon. During the research part of
these errors may be corrected, e.g. by changing the
transcriptions in the lexicon. If corrections are made, similar
corrections should also be made in the baseline system and
WERbegin should be calculated again. If this is not done, part
of the resulting improvement is due to the correction of errors
and possibly other sources. This makes it difficult to estimate
which part of the improvement is really due to the modeling of
Besides the presence of errors, other properties of the
canonical lexicon will also, to a large extent, determine the
amount of improvement obtained with a certain method. Let us
assume, for the sake of argument, that the canonical lexicon
contains pronunciations (i.e. transcriptions) for a certain
accent and speech style (e.g. read speech). A method is then
tested with a corpus that contains speech of another accent and
another speech style (e.g. conversational speech). The method
succeeds in improving the lexicon in the sense that the new
pronunciations in the lexicon are more appropriate for the
speech in the corpus, and a large improvement in the
performance is observed. Although it is clear that the method
has succeeded in modeling pronunciation variation, it is also
clear that the amount of improvement would have been (much)
smaller if the lexicon had contained more appropriate
transcriptions from the start and not those of another accent
and another speech type.
In short, a large amount of research and written explanation
is devoted to the reduction of WERend, while relatively little
effort is put in WERbegin. Since both quantities determine the
amount of improvement, and since the baseline systems differ
between studies, it becomes difficult to compare the various
3.4. Objective evaluation
The question that arises at this point is: Is an objective
evaluation and comparison of these methods at all possible?
This question is not easy to answer. An obvious solution
seems to be to use benchmark corpora and standard methods for
evaluation (e.g. to give everyone the same canonical lexicon),
like the NIST evaluations for automatic speech recognition and
automatic speaker verification. This would solve a number of
the problems mentioned above, but certainly not all of them.
The most important problem that remains is the choice of the
language. Like many other benchmark tests it could be
(American) English. However, pronunciation variation and the
ways in which it should be modeled can differ between
languages, as argued above. Furthermore, for various reasons it
would favor groups who do research on (American) English.
Finally, using benchmarks would not solve the problem of
differences between ASRs.
Still, the large scale (D)ARPA projects and the NIST
evaluations have shown that the combination of competition and
objective evaluation (i.e. the possibility to obtain an
objective comparison of methods) is very useful. Therefore, it
seems advisable to strive towards objective evaluation methods
within the field of pronunciation modeling. We should discuss
what kind of corpora and evaluation criteria could be used for
this purpose. The current workshop provides a good opportunity
for this discussion.
The research of Dr. H. Strik has been made possible by a
fellowship of the Royal Netherlands Academy of Arts and
Note: The first 26 references below are papers from the current
 M. Adda-Decker (1998) Pronunciation variants across
systems, languages and speaking style. This proc.
 M. Bacchiani, M. Ostendorf (1998) Joint acoustic unit
design and lexicon generation. This proc.
 K. Beulen, S. Ortmanns, A. Eiden, S. Martin, L. Welling,
J. Overmann, H. Ney (1998) Pronunciation modelling in
the RWTH large vocabulary speech recognizer. This proc.
 P. Bonaventura, F. Gallocchio, J. Mari, G. Micca (1998)
Speech recognition methods for non-native pronunciation
variations. This proc.
 N. Cremelie, J.-P. Martens (1998) In search of
pronunciation rules. This proc.
 J. Ferreiros, J. Mac¡as-Guarasa, J.M. Pardo, L.
Villarrubia (1998) Introducing multiple pronunciations
in spanish speech recognition systems. This proc.
 E. Fosler-Lussier, N. Morgan (1998) Effects of speaking
rate and word frequency on conversational
pronunciations. This proc.
 T. Fukada, T. Yoshimura, Y. Sagisaka (1998) Automatic
generation of multiple pronunciations based on neural
networks and language. This proc.
 S. Greenberg (1998) Speaking in shorthand: a syllable-centric perspective on understanding pronunciation. This
 H. Heine, G. Evermann, U. Jost (1998) An HMM-based
probabilistic lexicon. This proc.
 T. Holter, T. Svendsen (1998) Maximum likelihood
modelling of pronunciation variation. This proc.
 G. Lehtinen, S. Safra (1998) Generation and selection of
pronunciation variants for a flexible word recognizer.
 H. Mokbel, D. Jouvet (1998) Derivation of the optimal
phonetic transcription set for a word from its acoustic
realisations. This proc.
 F. Mouria-Beji (1998) Context and speed dependent
phonemic models for continuous speech recognition. This
 H.J. Nock, S.J. Young (1998) Detecting and correcting
poor pronunciations for multiword units. This proc.
 G. Perennou, L. Brieussel-Pousse (1998) Phonological
component in automatic speech recognition. This proc.
 S.D. Peters, P. Stubley (1998) Visualizing speech
trajectories. This proc.
 T.S. Polzin, A.H. Waibel (1998) Pronunciation variations
in emotional speech. This proc.
 M. Riley, W. Byrne, M. Finke, S. Khudanpur, A. Ljolje,
(1998) Stochastic pronunciation modeling from hand-labelled phonetic corpora. This proc.
 E.S. Ristad, P.N. Yianilos (1998) A surficial
pronunciation model. This proc.
 P. Roach, S. Arnfield (1998) Variation information in
pronunciation dictionaries. This proc.
 S. Safra, G. Lehtinen, K. Huber (1998) Modeling
pronunciation variations and coarticulation with finite-state . This proc.
 F. Schiel, A. Kipp, H.G. Tillmann (1998) Statistical
modelling of pronunciation: it's not the model, it's the
data. This proc.
 M. Wester, J.M. Kessens, H. Strik (1998) Improving the
performance of a Dutch CSR by modelling pronunciation
variation. This proc.
 G. Williams, S. Renals (1998) Confidence measures for
evaluating pronunciation models. This proc.
 R. Wiseman, S. Downey (1998) Dynamic and static
improvements to lexical baseforms. This proc.
 N. Cremelie & J.P. Martens (1995) On the use of
pronunciation rules for improved word recognition. Proc.
of Eurospeech-95, Madrid, Spain, Vol. III, pp. 1747-1750.
 N. Cremelie & J.P. Martens (1997) Automatic rule-based
generation of word pronunciation networks. Proc. of
EuroSpeech-97, pp. 2459-2462.
 L. Deng & D. Sun (1994) A statistical approach to
automatic speech recognition using the atomic speech
units constructed from overlapping articulatory
features. Journal of the Acoustical Society of America,
V95(5), May 1994, pp.2702-2719.
 N. Deshmukh, M. Weber & J. Picone (1996) Automated
generation of N-best pronunciations of proper nouns.
Proc. of ICASSP-96, Atlanta, Vol. 1, pp. 283-286.
 S. Downey & R. Wiseman (1997) Dynamic and static
improvements to lexical baseforms. Proc. of Eurospeech-97, Vol. 2, pp. 1027-1030.
 G. Flach (1995) Modelling pronunciation variability for
spectral domains. Proc. of Eurospeech-95, Madrid, Vol.
III, pp. 1743-1746.
 T. Fukada & Y. Sagisaka (1997) Automatic generation of a
pronunciation dictionary based on a pronunciation
network. Proc. of EuroSpeech-97, Rhodos, Vol. 5, pp.
 J.J. Godfrey, A. Ganapathiraju, C.S. Ramalingam & J.
Picone (1997) Microsegment-based connected digit
recognition. Proc. of ICASSP-97, Munich, Vol. 3, pp.
 T. Holter (1997) Maximum Likelihood Modelling of
Pronunciation in Automatic Speech Recognition PhD
thesis, Norwegian University of Science and Technology,
 J. Kessens & M. Wester (1997) Improving Recognition
Performance by Modelling Pronunciation Variation.
Proceedings of the CLS opening Academic Year '97-'98,
 A. Kipp, M.-B. Wesenick & F. Schiel (1996). Automatic
detection and segmentation of pronunciation variants in
German speech corpora. Proc. of ICSLP-96, Philadelphia,
 A. Kipp, M.-B. Wesenick & F. Schiel (1997).
Pronunciation Modeling Applied to Automatic Segmentation
of Spontaneous Speech. Proc. of EuroSpeech-97, Rhodes,
Vol. 2, pp. 1023-1026.
 L. Pousse & G. Perennou (1997) Dealing with
pronunciation variants at the language model level for
automatic continuous speech recognition of French. Proc.
of Eurospeech-97, Rhodes, Vol. 5, pp. 2727-2730.
 H. Strik (1998) Publications on pronunciation variation
and ASR. http://lands.let.ru.nl/Tspublic/ strik/pron-var/references.html
 D. Torre, L. Villarrubia, L. Hernandez & J.M. Elvira
(1997) Automatic Alternative Transcription Generation
and Vocabulary Selection for Flexible Word Recognizers.
Proc. of ICASSP-97, Munich, Vol. 2, pp. 1463-1466.
 M.-B. Wesenick (1996) Automatic generation of German
pronunciation variants. Proc. of ICSLP-96, Philadelphia,