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13. Lexicons

A Lexicon in Festival is a subsystem that provides pronunciations for words. It can consist of three distinct parts: an addenda, typically short consisting of hand added words; a compiled lexicon, typically large (10,000s of words) which sits on disk somewhere; and a method for dealing with words not in either list.

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13.1 Lexical entries

Lexical entries consist of three basic parts, a head word, a part of speech and a pronunciation. The headword is what you might normally think of as a word e.g. ‘walk’, ‘chairs’ etc. but it might be any token.

The part-of-speech field currently consist of a simple atom (or nil if none is specified). Of course there are many part of speech tag sets and whatever you mark in your lexicon must be compatible with the subsystems that use that information. You can optionally set a part of speech tag mapping for each lexicon. The value should be a reverse assoc-list of the following form

   '((( punc fpunc) punc)
     (( nn nnp nns nnps ) n)))

All part of speech tags not appearing in the left hand side of a pos map are left unchanged.

The third field contains the actual pronunciation of the word. This is an arbitrary Lisp S-expression. In many of the lexicons distributed with Festival this entry has internal format, identifying syllable structure, stress markigns and of course the phones themselves. In some of our other lexicons we simply list the phones with stress marking on each vowel.

Some typical example entries are

( "walkers" n ((( w oo ) 1) (( k @ z ) 0)) )
( "present" v ((( p r e ) 0) (( z @ n t ) 1)) )
( "monument" n ((( m o ) 1) (( n y u ) 0) (( m @ n t ) 0)) )

Note you may have two entries with the same headword, but different part of speech fields allow differentiation. For example

( "lives" n ((( l ai v z ) 1)) )
( "lives" v ((( l i v z ) 1)) )

See section Lookup process, for a description of how multiple entries with the same headword are used during lookup.

By current conventions, single syllable function words should have no stress marking, while single syllable content words should be stressed.

NOTE: the POS field may change in future to contain more complex formats. The same lexicon mechanism (but different lexicon) is used for holding part of speech tag distributions for the POS prediction module.

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13.2 Defining lexicons

As stated above, lexicons consist of three basic parts (compiled form, addenda and unknown word method) plus some other declarations.

Each lexicon in the system has a name which allows different lexicons to be selected from efficiently when switching between voices during synthesis. The basic steps involved in a lexicon definition are as follows.

First a new lexicon must be created with a new name

(lex.create "cstrlex")

A phone set must be declared for the lexicon, to allow both checks on the entries themselves and to allow phone mapping between different phone sets used in the system

(lex.set.phoneset "mrpa")

The phone set must be already declared in the system.

A compiled lexicon, the construction of which is described below, may be optionally specified

(lex.set.compile.file "/projects/festival/lib/dicts/cstrlex.out")

The method for dealing with unknown words, See section Letter to sound rules, may be set

(lex.set.lts.method 'lts_rules)
(lex.set.lts.ruleset 'nrl)

In this case we are specifying the use of a set of letter to sound rules originally developed by the U.S. Naval Research Laboratories. The default method is to give an error if a word is not found in the addenda or compiled lexicon. (This and other options are discussed more fully below.)

Finally addenda items may be added for words that are known to be common, but not in the lexicon and cannot reasonably be analysed by the letter to sound rules.

  '( "awb" n ((( ei ) 1) ((d uh) 1) ((b @ l) 0) ((y uu) 0) ((b ii) 1))))
  '( "cstr" n ((( s ii ) 1) (( e s ) 1) (( t ii ) 1) (( aa ) 1)) ))
  '( "Edinburgh" n ((( e m ) 1) (( b r @ ) 0))) ))

Using lex.add.entry again for the same word and part of speech will redefine the current pronunciation. Note these add entries to the current lexicon so its a good idea to explicitly select the lexicon before you add addenda entries, particularly if you are doing this in your own ‘.festivalrc’ file.

For large lists, compiled lexicons are best. The function lex.compile takes two filename arguments, a file name containing a list of lexical entries and an output file where the compiled lexicon will be saved.

Compilation can take some time and may require lots of memory, as all entries are loaded in, checked and then sorted before being written out again. During compilation if some entry is malformed the reading process halts with a not so useful message. Note that if any of your entries include quote or double quotes the entries will probably be misparsed and cause such a weird error. In such cases try setting

(debug_output t)

before compilation. This will print out each entry as it is read in which should help to narrow down where the error is.

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13.3 Lookup process

When looking up a word, either through the C++ interface, or Lisp interface, a word is identified by its headword and part of speech. If no part of speech is specified, nil is assumed which matches any part of speech tag.

The lexicon look up process first checks the addenda, if there is a full match (head word plus part of speech) it is returned. If there is an addenda entry whose head word matches and whose part of speech is nil that entry is returned.

If no match is found in the addenda, the compiled lexicon, if present, is checked. Again a match is when both head word and part of speech tag match, or either the word being searched for has a part of speech nil or an entry has its tag as nil. Unlike the addenda, if no full head word and part of speech tag match is found, the first word in the lexicon whose head word matches is returned. The rationale is that the letter to sound rules (the next defence) are unlikely to be better than an given alternate pronunciation for a the word but different part of speech. Even more so given that as there is an entry with the head word but a different part of speech this word may have an unusual pronunciation that the letter to sound rules will have no chance in producing.

Finally if the word is not found in the compiled lexicon it is passed to whatever method is defined for unknown words. This is most likely a letter to sound module. See section Letter to sound rules.

Optional pre- and post-lookup hooks can be specified for a lexicon. As a single (or list of) Lisp functions. The pre-hooks will be called with two arguments (word and features) and should return a pair (word and features). The post-hooks will be given a lexical entry and should return a lexical entry. The pre- and post-hooks do nothing by default.

Compiled lexicons may be created from lists of lexical entries. A compiled lexicon is much more efficient for look up than the addenda. Compiled lexicons use a binary search method while the addenda is searched linearly. Also it would take a prohibitively long time to load in a typical full lexicon as an addenda. If you have more than a few hundred entries in your addenda you should seriously consider adding them to your compiled lexicon.

Because many publicly available lexicons do not have syllable markings for entries the compilation method supports automatic syllabification. Thus for lexicon entries for compilation, two forms for the pronunciation field are supported: the standard full syllabified and stressed form and a simpler linear form found in at least the BEEP and CMU lexicons. If the pronunciation field is a flat atomic list it is assumed syllabification is required.

Syllabification is done by finding the minimum sonorant position between vowels. It is not guaranteed to be accurate but does give a solution that is sufficient for many purposes. A little work would probably improve this significantly. Of course syllabification requires the entry’s phones to be in the current phone set. The sonorant values are calculated from the vc, ctype, and cvox features for the current phoneset. See ‘src/arch/festival/Phone.cc:ph_sonority()’ for actual definition.

Additionally in this flat structure vowels (atoms starting with a, e, i, o or u) may have 1 2 or 0 appended marking stress. This is again following the form found in the BEEP and CMU lexicons.

Some example entries in the flat form (taken from BEEP) are

("table" nil (t ei1 b l))
("suspicious" nil (s @ s p i1 sh @ s))

Also if syllabification is required there is an opportunity to run a set of "letter-to-sound"-rules on the input (actually an arbitrary re-write rule system). If the variable lex_lts_set is set, the lts ruleset of that name is applied to the flat input before syllabification. This allows simple predictable changes such as conversion of final r into longer vowel for English RP from American labelled lexicons.

A list of all matching entries in the addenda and the compiled lexicon may be found by the function lex.lookup_all. This function takes a word and returns all matching entries irrespective of part of speech.

You can optionally intercept the words as they are looked up, and after they have been found through pre_hooks and post_hooks for each lexicon. This allows a function or list of functions to be applied to a word and feature before lookup or to the resulting entry after lookup. The following example shows how to add voice specific entries to a general lexicon without affecting other voices that use that lexicon.

For example suppose we were trying to use a Scottish English voice with the US English (cmu) lexicon. A number of entries will be inappropriate but we can redefine some entries thus

(set! cmu_us_awb::lexicon_addenda
	("edinburgh" n (((eh d) 1) ((ax n) 0) ((b r ax) 0)))
	("poem" n (((p ow) 1) ((y ax m) 0)))
	("usual" n (((y uw) 1) ((zh ax l) 0)))
	("air" n (((ey r) 1)))
	("hair" n (((hh ey r) 1)))
	("fair" n (((f ey r) 1)))
	("chair" n (((ch ey r) 1)))))

We can then define a function that checks to see if the word looked up is in the speaker specific exception list and use that entry instead.

(define (cmu_us_awb::cmu_lookup_post entry)
  "(cmu_us_awb::cmu_lookup_post entry)
Speaker specific lexicon addeda."
  (let ((ne 
	 (assoc_string (car entry) cmu_us_awb::lexicon_addenda)))
    (if ne

And then for the particular voice set up we need to add both a selection part and a reset part. Thus following the FestVox conventions for voice set up.

(define (cmu_us_awb::select_lexicon)

    (lex.select "cmu")
    ;; Get old var for reset and to append our function to is
    (set! cmu_us_awb::old_cmu_post_hooks 
       (lex.set.post_hooks nil))
       (append cmu_us_awb::old_cmu_post_hooks
               (list cmu_us_awb::cmu_lookup_post)))


(define (cmu_us_awb::reset_lexicon)

  ;; reset CMU's post_hooks back to original
  (lex.set.post_hooks cmu_us_awb::old_cmu_post_hooks)


The above isn’t the most efficient way as the word is looked up first then it is checked with the speaker specific list.

The pre_hooks functions are called with two arguments, the word and features, they should return a pair of word and features.

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13.4 Letter to sound rules

Each lexicon may define what action to take when a word cannot be found in the addenda or the compiled lexicon. There are a number of options which will hopefully be added to as more general letter to sound rule systems are added.

The method is set by the command

(lex.set.lts.method METHOD)

Where METHOD can be any of the following


Throw an error when an unknown word is found (default).


Use externally specified set of letter to sound rules (described below). The name of the rule set to use is defined with the lex.lts.ruleset function. This method runs one set of rules on an exploded form of the word and assumes the rules return a list of phonemes (in the appropriate set). If multiple instances of rules are required use the function method described next.


This returns an entry with a nil pronunciation field. This will only be valid in very special circumstances.


Call this as a LISP function function name. This function is given two arguments: the word and the part of speech. It should return a valid lexical entry.

The basic letter to sound rule system is very simple but is powerful enough to build reasonably complex letter to sound rules. Although we’ve found trained LTS rules better than hand written ones (for complex languages) where no data is available and rules must be hand written the following rule formalism is much easier to use than that generated by the LTS training system (described in the next section).

The basic form of a rule is as follows


This interpretation is that if ITEMS appear in the specified right and left context then the output string is to contain NEWITEMS. Any of LEFTCONTEXT, RIGHTCONTEXT or NEWITEMS may be empty. Note that NEWITEMS is written to a different "tape" and hence cannot feed further rules (within this ruleset). An example is

( # [ c h ] C = k )

The special character # denotes a word boundary, and the symbol C denotes the set of all consonants, sets are declared before rules. This rule states that a ch at the start of a word followed by a consonant is to be rendered as the k phoneme. Symbols in contexts may be followed by the symbol * for zero or more occurrences, or + for one or more occurrences.

The symbols in the rules are treated as set names if they are declared as such or as symbols in the input/output alphabets. The symbols may be more than one character long and the names are case sensitive.

The rules are tried in order until one matches the first (or more) symbol of the tape. The rule is applied adding the right hand side to the output tape. The rules are again applied from the start of the list of rules.

The function used to apply a set of rules if given an atom will explode it into a list of single characters, while if given a list will use it as is. This reflects the common usage of wishing to re-write the individual letters in a word to phonemes but without excluding the possibility of using the system for more complex manipulations, such as multi-pass LTS systems and phoneme conversion.

From lisp there are three basic access functions, there are corresponding functions in the C/C++ domain.

(lts.ruleset NAME SETS RULES)

Define a new set of lts rules. Where NAME is the name for this rule, SETS is a list of set definitions of the form (SETNAME e0 e1 ...) and RULES are a list of rules as described above.


Apply the set of rules named RULESETNAME to WORD. If WORD is a symbol it is exploded into a list of the individual characters in its print name. If WORD is a list it is used as is. If the rules cannot be successfully applied an error is given. The result of (successful) application is returned in a list.

(lts.check_alpha WORD RULESETNAME)

The symbols in WORD are checked against the input alphabet of the rules named RULESETNAME. If they are all contained in that alphabet t is returned, else nil. Note this does not necessarily mean the rules will successfully apply (contexts may restrict the application of the rules), but it allows general checking like numerals, punctuation etc, allowing application of appropriate rule sets.

The letter to sound rule system may be used directly from Lisp and can easily be used to do relatively complex operations for analyzing words without requiring modification of the C/C++ system. For example the Welsh letter to sound rule system consists or three rule sets, first to explicitly identify epenthesis, then identify stressed vowels, and finally rewrite this augmented letter string to phonemes. This is achieved by the following function

(define (welsh_lts word features)
  (let (epen str wel)
    (set! epen (lts.apply (downcase word) 'newepen))
    (set! str (lts.apply epen 'newwelstr))
    (set! wel (lts.apply str 'newwel))
    (list word
          (lex.syllabify.phstress wel))))

The LTS method for the Welsh lexicon is set to welsh_lts, so this function is called when a word is not found in the lexicon. The above function first downcases the word and then applies the rulesets in turn, finally calling the syllabification process and returns a constructed lexically entry.

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13.5 Building letter to sound rules

As writing letter to sound rules by hand is hard and very time consuming, an alternative method is also available where a latter to sound system may be built from a lexicon of the language. This technique has successfully been used from English (British and American), French and German. The difficulty and appropriateness of using letter to sound rules is very language dependent,

The following outlines the processes involved in building a letter to sound model for a language given a large lexicon of pronunciations. This technique is likely to work for most European languages (including Russian) but doesn’t seem particularly suitable for very language alphabet languages like Japanese and Chinese. The process described here is not (yet) fully automatic but the hand intervention required is small and may easily be done even by people with only a very little knowledge of the language being dealt with.

The process involves the following steps

All except the first two stages of this are fully automatic.

Before building a model its wise to think a little about what you want it to do. Ideally the model is an auxiluary to the lexicon so only words not found in the lexicon will require use of the letter to sound rules. Thus only unusual forms are likely to require the rules. More precisely the most common words, often having the most non-standard pronunciations, should probably be explicitly listed always. It is possible to reduce the size of the lexicon (sometimes drastically) by removing all entries that the training LTS model correctly predicts.

Before starting it is wise to consider removing some entries from the lexicon before training, I typically will remove words under 4 letters and if part of speech information is available I remove all function words, ideally only training from nouns verbs and adjectives as these are the most likely forms to be unknown in text. It is useful to have morphologically inflected and derived forms in the training set as it is often such variant forms that not found in the lexicon even though their root morpheme is. Note that in many forms of text, proper names are the most common form of unknown word and even the technique presented here may not adequately cater for that form of unknown words (especially if they unknown words are non-native names). This is all stating that this may or may not be appropriate for your task but the rules generated by this learning process have in the examples we’ve done been much better than what we could produce by hand writing rules of the form described in the previous section.

First preprocess the lexicon into a file of lexical entries to be used for training, removing functions words and changing the head words to all lower case (may be language dependent). The entries should be of the form used for input for Festival’s lexicon compilation. Specifical the pronunciations should be simple lists of phones (no syllabification). Depending on the language, you may wish to remve the stressing—for examples here we have though later tests suggest that we should keep it in even for English. Thus the training set should look something like

("table" nil (t ei b l))
("suspicious" nil (s @ s p i sh @ s))

It is best to split the data into a training set and a test set if you wish to know how well your training has worked. In our tests we remove every tenth entry and put it in a test set. Note this will mean our test results are probably better than if we removed say the last ten in every hundred.

The second stage is to define the set of allowable letter to phone mappings irrespective of context. This can sometimes be initially done by hand then checked against the training set. Initially constract a file of the form

(require 'lts_build)
(set! allowables 
      '((a _epsilon_)
        (b _epsilon_)
        (c _epsilon_)
        (y _epsilon_)
        (z _epsilon_)
        (# #)))

All letters that appear in the alphabet should (at least) map to _epsilon_, including any accented characters that appear in that language. Note the last two hashes. These are used by to denote beginning and end of word and are automatically added during training, they must appear in the list and should only map to themselves.

To incrementally add to this allowable list run festival as

festival allowables.scm 

and at the prompt type

festival> (cummulate-pairs "oald.train")

with your train file. This will print out each lexical entry that couldn’t be aligned with the current set of allowables. At the start this will be every entry. Looking at these entries add to the allowables to make alignment work. For example if the following word fails

("abate" nil (ah b ey t)) 

Add ah to the allowables for letter a, b to b, ey to a and t to letter t. After doing that restart festival and call cummulate-pairs again. Incrementally add to the allowable pairs until the number of failures becomes accceptable. Often there are entries for which there is no real relationship between the letters and the pronunciation such as in abbreviations and foreign words (e.g. "aaa" as "t r ih p ax l ey"). For the lexicons I’ve used the technique on less than 10 per thousand fail in this way.

It is worth while being consistent on defining your set of allowables. (At least) two mappings are possible for the letter sequence ch—having letter c go to phone ch and letter h go to _epsilon_ and also letter c go to phone _epsilon_ and letter h goes to ch. However only one should be allowed, we preferred c to ch.

It may also be the case that some letters give rise to more than one phone. For example the letter x in English is often pronunced as the phone combination k and s. To allow this, use the multiphone k-s. Thus the multiphone k-s will be predicted for x in some context and the model will separate it into two phones while it also ignoring any predicted _epsilons_. Note that multiphone units are relatively rare but do occur. In English, letter x give rise to a few, k-s in taxi, g-s in example, and sometimes g-zh and k-sh in luxury. Others are w-ah in one, t-s in pizza, y-uw in new (British), ah-m in -ism etc. Three phone multiphone are much rarer but may exist, they are not supported by this code as is, but such entries should probably be ignored. Note the - sign in the multiphone examples is significant and is used to indentify multiphones.

The allowables for OALD end up being

(set! allowables 
      ((a _epsilon_ ei aa a e@ @ oo au o i ou ai uh e)
       (b _epsilon_ b )
       (c _epsilon_ k s ch sh @-k s t-s)
       (d _epsilon_ d dh t jh)
       (e _epsilon_ @ ii e e@ i @@ i@ uu y-uu ou ei aa oi y y-u@ o)
       (f _epsilon_ f v )
       (g _epsilon_ g jh zh th f ng k t)
       (h _epsilon_ h @ )
       (i _epsilon_ i@ i @ ii ai @@ y ai-@ aa a)
       (j _epsilon_ h zh jh i y )
       (k _epsilon_ k ch )
       (l _epsilon_ l @-l l-l)
       (m _epsilon_ m @-m n)
       (n _epsilon_ n ng n-y )
       (o _epsilon_ @ ou o oo uu u au oi i @@ e uh w u@ w-uh y-@)
       (p _epsilon_ f p v )
       (q _epsilon_ k )
       (r _epsilon_ r @@ @-r)
       (s _epsilon_ z s sh zh )
       (t _epsilon_ t th sh dh ch d )
       (u _epsilon_ uu @ w @@ u uh y-uu u@ y-u@ y-u i y-uh y-@ e)
       (v _epsilon_ v f )
       (w _epsilon_ w uu v f u)
       (x _epsilon_ k-s g-z sh z k-sh z g-zh )
       (y _epsilon_ i ii i@ ai uh y @ ai-@)
       (z _epsilon_ z t-s s zh )
       (# #)

Note this is an exhaustive list and (deliberately) says nothing about the contexts or frequency that these letter to phone pairs appear. That information will be generated automatically from the training set.

Once the number of failed matches is signficantly low enough let cummulate-pairs run to completion. This counts the number of times each letter/phone pair occurs in allowable alignments.

Next call

festival> (save-table "oald-")

with the name of your lexicon. This changes the cummulation table into probabilities and saves it.

Restart festival loading this new table

festival allowables.scm oald-pl-table.scm

Now each word can be aligned to an equally-lengthed string of phones, epsilon and multiphones.

festival> (aligndata "oald.train" "oald.train.align")

Do this also for you test set.

This will produce entries like

aaronson _epsilon_ aa r ah n s ah n
abandon ah b ae n d ah n
abate ah b ey t _epsilon_
abbe ae b _epsilon_ iy

The next stage is to build features suitable for ‘wagon’ to build models. This is done by

festival> (build-feat-file "oald.train.align" "oald.train.feats")

Again the same for the test set.

Now you need to constructrure a description file for ‘wagon’ for the given data. The can be done using the script ‘make_wgn_desc’ provided with the speech tools

Here is an example script for building the models, you will need to modify it for your particualr database but it shows the basic processes

for i in a b c d e f g h i j k l m n o p q r s t u v w x y z 
   # Stop value for wagon
   echo letter $i STOP $STOP
   # Find training set for letter $i
   cat oald.train.feats |
    awk '{if ($6 == "'$i'") print $0}' >ltsdataTRAIN.$i.feats
   # split training set to get heldout data for stepwise testing
   traintest ltsdataTRAIN.$i.feats
   # Extract test data for letter $i
   cat oald.test.feats |
    awk '{if ($6 == "'$i'") print $0}' >ltsdataTEST.$i.feats
   # run wagon to predict model
   wagon -data ltsdataTRAIN.$i.feats.train -test ltsdataTRAIN.$i.feats.test \
          -stepwise -desc ltsOALD.desc -stop $STOP -output lts.$i.tree
   # Test the resulting tree against
   wagon_test -heap 2000000 -data ltsdataTEST.$i.feats -desc ltsOALD.desc \
              -tree lts.$i.tree

The script ‘traintest’ splits the given file ‘X’ into ‘X.train’ and ‘X.test’ with every tenth line in ‘X.test’ and the rest in ‘X.train’.

This script can take a significnat amount of time to run, about 6 hours on a Sun Ultra 140.

Once the models are created the must be collected together into a single list structure. The trees generated by ‘wagon’ contain fully probability distributions at each leaf, at this time this information can be removed as only the most probable will actually be predicted. This substantially reduces the size of the tress.

(merge_models 'oald_lts_rules "oald_lts_rules.scm")

(merge_models is defined within ‘lts_build.scm’) The given file will contain a set! for the given variable name to an assoc list of letter to trained tree. Note the above function naively assumes that the letters in the alphabet are the 26 lower case letters of the English alphabet, you will need to edit this adding accented letters if required. Note that adding "’" (single quote) as a letter is a little tricky in scheme but can be done—the command (intern "'") will give you the symbol for single quote.

To test a set of lts models load the saved model and call the following function with the test align file

festival oald-table.scm oald_lts_rules.scm
festival> (lts_testset "oald.test.align" oald_lts_rules)

The result (after showing all the failed ones), will be a table showing the results for each letter, for all letters and for complete words. The failed entries may give some notion of how good or bad the result is, sometimes it will be simple vowel diferences, long versus short, schwa versus full vowel, other times it may be who consonants missing. Remember the ultimate quality of the letter sound rules is how adequate they are at providing acceptable pronunciations rather than how good the numeric score is.

For some languages (e.g. English) it is necessary to also find a stree pattern for unknown words. Ultimately for this to work well you need to know the morphological decomposition of the word. At present we provide a CART trained system to predict stress patterns for English. If does get 94.6% correct for an unseen test set but that isn’t really very good. Later tests suggest that predicting stressed and unstressed phones directly is actually better for getting whole words correct even though the models do slightly worse on a per phone basis black98.

As the lexicon may be a large part of the system we have also experimented with removing entries from the lexicon if the letter to sound rules system (and stree assignment system) can correct predict them. For OALD this allows us to half the size of the lexicon, it could possibly allow more if a certain amount of fuzzy acceptance was allowed (e.g. with schwa). For other languages the gain here can be very signifcant, for German and French we can reduce the lexicon by over 90%. The function reduce_lexicon in ‘festival/lib/lts_build.scm’ was used to do this. A diccussion of using the above technique as a dictionary compression method is discussed in pagel98. A morphological decomposition algorithm, like that described in black91, may even help more.

The technique described in this section and its relative merits with respect to a number of languages/lexicons and tasks is dicussed more fully in black98.

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13.6 Lexicon requirements

For English there are a number of assumptions made about the lexicon which are worthy of explicit mention. If you are basically going to use the existing token rules you should try to include at least the following in any lexicon that is to work with them.

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13.7 Available lexicons

Currently Festival supports a number of different lexicons. They are all defined in the file ‘lib/lexicons.scm’ each with a number of common extra words added to their addendas. They are


The Computer Users Version of Oxford Advanced Learner’s Dictionary is available from the Oxford Text Archive ftp://ota.ox.ac.uk/pub/ota/public/dicts/710. It contains about 70,000 entries and is a part of the BEEP lexicon. It is more consistent in its marking of stress though its syllable marking is not what works best for our synthesis methods. Many syllabic ‘l’’s, ‘n’’s, and ‘m’’s, mess up the syllabification algorithm, making results sometimes appear over reduced. It is however our current default lexicon. It is also the only lexicon with part of speech tags that can be distributed (for non-commercial use).


This is automatically constructed from ‘cmu_dict-0.4’ available from many places on the net (see comp.speech archives). It is not in the mrpa phone set because it is American English pronunciation. Although mappings exist between its phoneset (‘darpa’) and ‘mrpa’ the results for British English speakers are not very good. However this is probably the biggest, most carefully specified lexicon available. It contains just under 100,000 entries. Our distribution has been modified to include part of speech tags on words we know to be homographs.


A version of the CSTR lexicon which has been floating about for years. It contains about 25,000 entries. A new updated free version of this is due to be released soon.


A British English rival for the ‘cmu_lex’. BEEP has been made available by Tony Robinson at Cambridge and is available in many archives. It contains 163,000 entries and has been converted to the ‘mrpa’ phoneset (which was a trivial mapping). Although large, it suffers from a certain randomness in its stress markings, making use of it for synthesis dubious.

All of the above lexicons have some distribution restrictions (though mostly pretty light), but as they are mostly freely available we provide programs that can convert the originals into Festival’s format.

The MOBY lexicon has recently been released into the public domain and will be converted into our format soon.

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13.8 Post-lexical rules

It is the lexicon’s job to produce a pronunciation of a given word. However in most languages the most natural pronunciation of a word cannot be found in isolation from the context in which it is to be spoken. This includes such phenomena as reduction, phrase final devoicing and r-insertion. In Festival this is done by post-lexical rules.

PostLex is a module which is run after accent assignment but before duration and F0 generation. This is because knowledge of accent position is necessary for vowel reduction and other post lexical phenomena and changing the segmental items will affect durations.

The PostLex first applies a set of built in rules (which could be done in Scheme but for historical reasons are still in C++). It then applies the functions set in the hook postlex_rules_hook. These should be a set of functions that take an utterance and apply appropriate rules. This should be set up on a per voice basis.

Although a rule system could be devised for post-lexical sound rules it is unclear what the scope of them should be, so we have left it completely open. Our vowel reduction model uses a CART decision tree to predict which syllables should be reduced, while the "’s" rule is very simple (shown in ‘festival/lib/postlex.scm’).

The 's in English may be pronounced in a number of different ways depending on the preceding context. If the preceding consonant is a fricative or affricative and not a palatal labio-dental or dental a schwa is required (e.g. bench's) otherwise no schwa is required (e.g. John's). Also if the previous phoneme is unvoiced the "s" is rendered as an "s" while in all other cases it is rendered as a "z".

For our English voices we have a lexical entry for "’s" as a schwa followed by a "z". We use a post lexical rule function called postlex_apos_s_check to modify the basic given form when required. After lexical lookup the segment relation contains the concatenation of segments directly from lookup in the lexicon. Post lexical rules are applied after that.

In the following rule we check each segment to see if it is part of a word labelled "’s", if so we check to see if are we currently looking at the schwa or the z part, and test if modification is required

(define (postlex_apos_s_check utt)
  "(postlex_apos_s_check UTT)
Deal with possesive s for English (American and British).  Delete
schwa of 's if previous is not a fricative or affricative, and
change voiced to unvoiced s if previous is not voiced."
   (lambda (seg)
     (if (string-equal "'s" (item.feat 
                             seg "R:SylStructure.parent.parent.name"))
         (if (string-equal "a" (item.feat seg 'ph_vlng))
             (if (and (member_string (item.feat seg 'p.ph_ctype) 
                                     '(f a))
                      (not (member_string
                            (item.feat seg "p.ph_cplace") 
                            '(d b g))))
                 t;; don't delete schwa
                 (item.delete seg))
             (if (string-equal "-" (item.feat seg "p.ph_cvox"))
                 (item.set_name seg "s")))));; from "z"
   (utt.relation.items utt 'Segment))

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