Accent and boundary tones are what we will use, hopefully in a theory
independent way, to refer to the two main types of intonation event.
For English, and for many other languages the prediction of position of
the accents and boundaries can be done as an independent process from F0
contour generation itself. This is definite true from the major theories
we will be considering.
As with phrase break prediction there are some simple rules that will go
a surprisingly long way. And as with most of the other statistical
learning techniques simple rules cover most of the work, more complex
rules work better, but the best results are from using the sorts of
information you were using in rules but statistically training them from
a appropriate data.
For English the placement of accents on stressed syllables in
all content words is a quite reasonable approximation achieving
about 80% accuracy on typical databases. [hirschberg90]
is probably the best example of a detailed rule driven approach
(for English). CART trees based on the sorts of features Hirschberg
uses are quite reasonable. Though eventual these rules become
limiting and a richer knowledge source is required to assign
accent patterns to complex nominals (see [sproat90}].
However all these techniques quickly come to the stumbling block
that although simple so-called discourse neutral intonation is
relatively easy achieve, achieving realistic, natural accent
placement is still beyond our synthesis systems (though perhaps
not for much longer).
The simplest rule for English may be reasonable for other
languages. There are even simpler solutions to this, such
as fixed prosody, or fixed declination, but apart from
debugging a voice these are simpler than is required even
for the most basic voices.
For English, adding a simple hat accent on lexically stressed syllables
in all content words works surprisingly well. To do this in Festival you
need a CART tree to predict accentedness, and rules to add the hat
accent (though we will leave out F0 generation until the next section).
A basic tree that predicts accents of stressed syllables
in content words is
(set! simple_accent_cart_tree
'
(
(R:SylStructure.parent.gpos is content)
( (stress is 1)
((Accented))
((NONE))
)
)
)
The above tree simply distinguishes accented syllables from
non-accented. In theories like ToBI [
silverman92], a number of
different types of accent are supported. ToBI, with variations, has been
applied to a number of languages and may be suitable for yours.
However, although accent and boundary types have been identified for
various languages and dialects, a computational mechanism for generating
and F0 contour from an accent specification often has not yet been
specified (we will discuss this more fully below).
If the above is considered too naive a more elaborate hand specified
tree can also be written, using relevant factors, probably similar to
those used in [hirschberg90]. Following that, training from
data is the next option. Assuming a database exists and has been
labeled with discrete accent classifications, we can extract
data from it for training a CART tree with wagon. We will
build the tree in festival/accents/. First we need
a file listing the features that are felt to affect accenting.
For this we will predict accents on syllables as that has been
used for the English voices created so far, but there is an
argument for predict accent placement on a word basis as although
accents will need to be syllable aligned, which syllable in a
word gets the accent is reasonably well defined (at least compared
with predicting accent placement).
A possible list of features for accent prediction is
put in the file accent.feats.
R:Intonation.daughter1.name
R:SylStructure.parent.R:Word.p.gpos
R:SylStructure.parent.gpos
R:SylStructure.parent.R:Word.n.gpos
ssyl_in
syl_in
ssyl_out
syl_out
p.stress
stress
n.stress
pp.syl_break
p.syl_break
syl_break
n.syl_break
nn.syl_break
pos_in_word
position_type
We can extract these features from the utterances using the
Festival script
dumpfeats
dumpfeats -feats accent.feats -relation Syllable \
-output accent.data ../utts/*.utts
We now need a description file for the features which can be
approximated by the speech tools script
make_wagon_desc
make_wagon_desc accent.data accent.feat accent.desc
Because this script cannot determine if a feature is categorical,
if takes an range of values you must hand edit the output
file and change any feature to
float or
int if that is what
it is.
The next stage is to split the data into training and test sets. If
stepwise training is to be used for building the CART tree (which is
recommended) then the training data should be further split
traintest accent.data
traintest accent.data.train
Deciding on a stop value for training depends on the number of
examples, though this can be tuned to ensure over-training isn't
happening.
wagon -data accent.data.train.train -desc accent.desc \
-test accent.data.train.test -stop 10 -stepwise -output accent.tree
wagon_test -data accent.data.test -desc accent.desc \
-tree accent.tree
This above is designed to predict accents, and similar tree should be
used to predict boundary tones as well. For the most part intonation
boundaries are defined to occur at prosodic phrase boundaries so that
task is somewhat easier, though if you have a number of boundary tone
types in your inventory then the prediction is not so straightforward.
When training ToBI type accent types it is not easy to get the right
type of variation in the accent types. Although some ToBI labels have
been associated with semantic intentions and including discourse
information has been shown help prediction (e.g. [black97a}],
getting this acceptably correct is not easy. Various techniques
in modifying the training data do seem to help. Because of the
low incidence of "L*" labels in at least the f2b data,
duplicating all sample points in the training data with L's
does increase the likelihood of prediction and does seem to give
a more varied distribution. Alternatively wagon returns a
probability distribution for the accents, normally the most
probable is selected, this could be modified to select from
the distribution randomly based on their probabilities.
Once trees have been built they can be used in a voices
as follows. Within the voice definition function
(set! int_accent_cart_tree simple_accent_cart_tree)
(set! int_tone_cart_tree simple_tone_cart_tree)
(Parameter.set 'Int_Method Intonation_Tree)
or if only one tree is required you can use the simpler
intonation method
(set! int_accent_cart_tree simple_accent_cart_tree)
(Parameter.set 'Int_Method Intonation_Simple)
