Sylli: Automatic Phonological Syllabification for ItalianLuca Iacoponi1 , Renata Savy2
1

2

Department of Linguistics, University of Pisa, Italy
Department of Humanities, University of Salerno, Italy
jacoponi@gmail.com, rsavy@unisa.it

Abstract
We will present a complete syllabifier for Italian (Sylli), thatis based on phonological principles, flexible and easy to adapt
for other uses, alphabets and languages. Crucial concepts regarding syllabification principles in modern phonological theory will be discussed (§1.1); specific issues concerning Italiansyllabification will then be summarised (§1.2) and an overview
of the available automatic syllabification models will be provided (§1.3). We will then move on to describe the program
structure, the syllabification algorithm and two particular issues
concerning syllabification in Italian (§2). Finally, we will illustrate the results of a manual syllabification test carried out bylinguists to verify the accuracy of the algorithm (§3).
Index Terms: automatic syllabification, syllabifier, syllable,
Italian

1. Introduction
The aim of this paper is to present a complete syllabifier for
Italian that, unlike other available options, can be founded onphonological principles and architectural constraints and could
therefore be flexible and easy to adapt for other uses, alphabetsand languages. In order to describe the internal structure andoperational rules of our system, we will briefly outline somebasic phonological concepts regarding the syllable and syllabification were implemented in the algorithm.
1.1. Syllabification Principles
In theoretical linguistics, the syllable has almost always been
considered an essential phonological unit with respect toprosody, phonotactics and phonological processing. The roleof the syllable in modern linguistic theory has been central especially since the rise of non-linear phonologies in the eighties, and became even more prominent and controversial withthe development of later theories based on the autosegmental
framework [1] as well as in Optimality Theory (OT) [2].
In speech technologies, the syllable began taking grounda decade after theoretical phonology. Syllabic units have beenused in place of individual segments or sub-segments in speech
recognition (SR) and text-to-speech systems (TTS) and as feeding units in Artificial Neural Networks (ANNs) [3][4][5].
The first syllabification principle ever recognised was theSonority Sequencing Principle (SSP) [6]. The SSP is based on
the Sonority Hierarchy (SH), which ranks segments by their intrinsic sonority; the SSP states that between any member of asyllable and the syllable peak, only sounds of higher sonority
rank are permitted [7]. Sonority Distance principles (SD) arebased on the SSP and define a minimum threshold in sonority
differences [8]. A consonant cluster violates the sonority principle and is heterosyllabic only if the difference α between twosegments is minor than a language-specific threshold. VCCxV
will be syllabified as VC.Cx if the sonority of C1 ≥ α, V.CCxV
if C1 − C2 > α. The SSP and the SD have changed form, andadapted with respect to the phonological theory they were borrowed into.
Another group of syllabification principles relates to thephonotactics of languages. Possible codas and onsets are defined as possible word-initial or word-final clusters [9]. This
principle is based on two assumptions: 1) that only a medial cluster that could be analysed as a word-final followed byword-initial cluster exists in language and 2) that the speaker’s
intuition tends to identify units that match phonotactic constraints. The SSP is combined with the Maximum Onset Principle (MOP) [9], which regulates the distribution of ambiguousintervocalic clusters. In a sequence VCCV the application of the
MOP results in V.CCV if CCV is a possible word-initial cluster
or VC.CV.
1.2. Syllabification in Italian
Italian is one of the languages which is claimed to respect the
SSP (or its possible variants). The literature on Italian syllabification has focussed on two issues: the sC clusters and the status
of vowels. It is claimed that sC clusters in Italian (and universally) are heterosyllabic [10], but some authors argue that theycan either be tautosyllabic or that they at least may show somedegree of variation [11]. For example, in the word pasta, twosyllabifications are possible: the heterosyllabic ‘pas.ta’ and thetautosyllabic ‘pa.sta’. Accepting the heterosyllabic proposal,
forces the first segment to be extra-syllabic (or preceded by an
empty nucleus) in words such as stella ‘star’ , which then gives
the syllabification ‘s.tel.la’.
The other problem of Italian syllabification is the directconsequence of the status of glides. It is argued that glides are
not distinctive in Italian [12], but are instead derived from thelenition of velar and palatal high vowels; such hypothesis can be
confuted on the ground of internal evidence. There are in factcases of surface glides in stressed syllables, where vowel lenition should not occur, as in chiocciola ["kjOttSola] ‘snail’ [13].
It will then have to be assumed that glides can be either derived
or underlying, but as we will show this distinction has no consequence on the syllabification algorithm.
1.3. Automatic Syllabification
In the literature a distinction is made between rule-based anddata-driven syllabifiers [14]. Since most current phonologicaltheories criticise the use of rules in favour of rich representations or constraints, we mantain that it is no longer accurate torefer to these models in such terms. We here propose to distinguish between data-driven and algorithmic approaches; algorithmic approaches have developed along the lines of theoreticallinguistic research. Recently, many syllabification algorithms
have been developed in the OT framework. One of these [15]evaluates the candidate set using a classical set of constraints(PARSE  NOONSET  ONSET) by assigning four candidates to each segment: onset, coda, nucleus and unsyllabified.
Offending candidates are then excluded from the set by a nonlinear evaluation procedure. Other proposals include Fisher’salgorithm1 (based on Kahn’s hypothesis) and all algorithms thatparse the input and apply a rich set of language-specific rules[16][17].
For data-driven models, many ANNs have been used forsyllable division tasks, such as generic neural algorithm [18],dynamic systems [19] and recursive networks [4]. Data-driven
approaches not using ANNs include look-up procedures, syllabification by analogy [14] and exemplar-based generalisation
techniques [3].
An SSP based solution designed specifically for Italian canbe considered a hybrid that combines the SSP, the MOP and an
exception-handling mechanism [20]. This algorithm parses astring, find the least sonorous segment and for each sonorous
segment adds a syllable boundary after the segment (if it is asonorant, otherwise it adds the boundary before it). Another algorithmic approach is a rule listing algorithm [21]. A binary decision tree parses the input string from left to right and decides
whether a string is heterosyllabic or tautosyllabic by meansof matching rules. Data-driven models for Italian have beenused to explore particular phonotactic phenomena. The basic
assumption is that syllabification is governed by the speaker’s
phonotactic competence. Clusters which occur together more
frequently hold a stronger attraction and therefore are tautosyllabic. The syllable is then composed of groups of segments
which are strongly attracted to one another [22].

2. Sylli
2

The Sylli algorithm was designed as a modular system, with
one simple, fast, specific and universal module made up of the
Syllabification Algorithm (SA henceforth), other computational
satellites which provide input-output specific processing (transducers) and a static language-specific vocabulary. Differencesin syllabification are managed by changes in both the transducers or the SH. This architecture also has the advantage of an
implementation that is flexible and easy to adapt to different alphabets and languages. We will first describe the structure of
the system and provide a description of its components (§2.1);
then, the syllabification algorithm is presented (§2.2).
2.1. System Structure
The system is composed of two transducers (one for the input
and one for the output), the syllabification algorithm and themapping list (i.e., the vocabulary). The two transducers convert
the two-dimensional linear input to a three-dimensional phonological form that is necessary for the processing in the phonological module and then sends the phonological form back into alinear string for output printing. The transducers can be thoughtof as a modular translator, that allows the phonological module
to operate only on a closed set of symbols, a language-specificvocabulary, and the output to get rid of all phonological information necessary for phonological processing. The transduc1 ftp://jaguar.ncsl.nist.gov/pub/tsylb2-1.1.tar.Z (last accessed 2
February 2011)
2 http://sylli.sf.net. The code is released under the Apache 2 licenceand it is free to download, use and redistribute.

Table 1: Sonority Hierarchy for Italian.
Class
VowelsGlidesSonorants + /s/
Fricatives
Stops

Sonority
54321

ers only define the mapping between two forms, and can therefore perform only two operations: Translate, which transforms
a symbol into a phonological item, an output form, or to zero;Send, which sends the translated content to the syllabification
module or to the output.
The transducers use vocabularies, which map input forms
A to objects B; for the purpose of syllabification, the vocabulary only contains the phonological form, the sonority of thesegment and its natural class. For example, the mapping entry
for the segment /a/ is the following: a = a, 22, V. Vocabularies
are specified in a configuration file and they can be switchedbetween, edited or created using the GUI or a text editor.
2.2. Syllabification Algorithm
The SA is an improvement on another SSP-based algorithm
developed for Italian [20]; it implements the SSP without
segment-specific exceptions and account for the problem of sC
and vowel clusters. The syllabification algorithm is very simple: if there is a minimum sonority peak, put a syllable boundary (Algorithm 1). Segment sonority attributes are compared ina left-to-right fashion. The string to syllabify only consists ofan array of phonological objects as specified in the transducer
vocabulary.
To summarise this process, first the transducer parses the input, that is converted using the vocabulary until a sequence thattriggers Send is found. Then the transducer sends the sequence
to the SA, where it gets syllabified. Finally, the syllabified sequence sent to the output transducers, which converts the output
into the appropriate form for printing.
Algorithm 1 The syllabification algorithm
Require: list of segments
1: for all segments do2:
son ← sonority(segment)
3:
if son is minimum peak OR son = sonority(segment-1)
then4:
put a syllable boundary
5:
end if
6: end for7: return list of syllable boundaries
The particular SH we used (see table 1) differs from thosefound in the literature in two aspects: the sibilant has a particular sonority; and the sonority classes differ from the traditional
natural phonological classes. No further restriction, constraint
or repair mechanism are needed, thus accomplishing our goal
to keep the syllabification algorithm general and simple. A
demonstration of the application of the algorithm is shown intable 2, where a set of clusters representative of Italian phonotactics have been syllabified by the algorithm3 .
3 Transcrips in test data and in table 2 are in SAMPA alphabet [23].

Table 2: Syllabification examples
Sequence
pane
aglio
aja
per
bacio
pasta
strano
zio
pazzo
gatto
paura
aiuolanafta

Syllabification
pa.ne
aL.Lo
a.ja
per
ba.tSopas.tas.tra.notsi.o
pat.tsogat.topa.u.raa.jwO.lanaf.taph-class
OV.NV
VS.SV
V.GV
OVR
OV.OV
OVO.OVO.OSV.NV
OV.V
OVO.OVOVO.OV
OV.V.SAV.GGV.SV
NVO.OV

CVCV
CV.CV
VC.CV
V.CV
CVC
CV.CV
CVC.CV
C.CCV.CV
CV.V
CVC.CV
CVC.CV
CV.V.SV
V.CCV.CV
CVC.CVIn section 1 we discussed two cases when syllabification in
Italian might be controversial. Vowel clusters and sC clusters
are an example of how Sylli can handle variation in syllabification through changes made solely the SH.
With the sC cluster, the sonority of the sibilant was changed
from fricatives (2) to sonorants (3); that was sufficient to predictthe heterosyllabic interpretation and the extrasyllabic segment
as theorised in phonology. The output is then: pasta → ‘pas.ta’;
stella → ‘s.tel.la’. On the other hand, tautosyllabic clusters are
predicted by assigning a zero-sonority to the sibilant: pasta →‘pa.sta’; stella → ‘stel.la’. A change in the SH is sufficient tohandle the distribution of sC clusters.
The hiatus or diphthong dilemma is not a direct problem of
the syllabification algorithm itself, because at any point in theprocessing (both for derived and underlying glides) the segment
must be syllabified according to its current status. Given a cluster of two vowels (two nuclei) the syllabification will end up
in a hiatus (heterosyllabic cluster). Otherwise, any sequence ofglide plus vowel, or vowel plus glide will result in a diphthong(tautosyllabic cluster)4 .

3. Test and Results
Some recent papers [14][25] tried to demonstrate that datadriven models are more accurate than rule-based models bycomparing the syllabification programs at hand over syllabifications as present in dictionaries; but we think that this methodology may miss one fundamental aspect concerning syllabification. Two objects of a very different nature are being compared: a natural language process and the output of the application of a normative set of rules. Data-driven methods undoubtedly perform better in such a task, because they guess the syllabification patterns used in dictionaries (which in turn partiallyrefers to phonological as well as orthographic, etymological andmorphological normative analyses) while rule-based models do
not try at all to simulate this kind of dictionary syllabification.
There are of course cases when orthographic syllabification isrequired, but these two types of syllabifications are to be kept
distinguished and are in no case comparable [25].
For these reasons we propose a manual verification test.
Three linguists were asked to manually syllabify a portion ofa corpus and the resulting syllables were then compared with
automatic syllabification obtained from the same corpus. The
4 No assumption is made on the internal constituency of the diphthongs [24].

test aims to verify the accuracy of the syllabifier and not to investigate the linguistic competence of the speakers, so some explicit guidelines were given (§3.2) for sC cluster syllabificationand for corpus encoding conventions. We will first discuss thedata used (§3.1), the test design (§3.2), and finally present theobtained results (§3.3).
3.1. Materials
All testing material was taken from CLIPS [26], a corpus of spoken Italian. CLIPS contains time-aligned phonetic and phonological transcription of approximately one hundred hours ofspeech recording, and is divided into five sub-corpora5 . In
this work, we will consider the phonological transcripts (STD
layer [27]) of dialogue and ortho-phonic sections of the corpus. These could be taken as representatives of the two ends
of a naturalness spectrum: the dialogue corpus is made of semispontaneous speech, while the ortho-phonic one is the most artificial, as it consists of sentence lists read by professional speakers, and made up to cover the widest range of Italian phonotactic
clusters.
The STD layer consists of time-aligned citation forms, enriched with lexical, non-lexical and segmentation symbols [27].
The symbols in the corpus had to be handled with the twoprocedures implemented in the transducers (§2.1). In particular, pauses in the speech and major syntactic boundaries triggered Send, thus forcing a syllable boundary while other symbols were not translated at all (translation to zero) as they did
not affect syllabification. The latter set includes word boundary markers (blank spaces), because in Italian they are almost
always context for resyllabification when not strengthened byother prosodic or syntactic boundaries.
3.2. Methods
To carry out the manual syllabification, three undergraduate linguistics students of the University of Pisa were asked to syllabify five random sentences at least ten words long from thedialogue sub-corpus and, for phonotactic covering, five sentences in the orthphonic corpus. The students were first asked
to syllabify two example sentences in a training session in order
to become familiar with the corpus coding. Arbitrary or ambiguous cases were resolved in the following instructions: resyllabification always applies across word boundaries, but not
across pauses or other major syntactic boundaries; sC clusters
are heterosyllabic, and always syllabify according to the given
transcription.
3.3. Test Results
In a total of 1008 syllables, the automatic syllabification was
faithful to the manual one (see table 3). In the dialogue corpus syllabification, subject A obtains a ration of 1.0, while
the other two make minor mistakes, including the missing resyllabification of a sC cluster and two hiatus syllabified as tautosyllabic, but always obtaining a ratio > 0.98. The ortho-phonic
test mirrors the results of the dialogue. The data shows a highratio of accuracy and the few differences result again from themissing application of re-syllabification across word boundaries
and in the non-native [km] cluster in the word acme.
These results show that the program syllabification is very
close to those made by human experts. Almost every minor
5 Dialogues, Radio and Television, Telephonic, Read speech, Orthophonic.

Table 3: Syllabification test results.
Subject
A
B
C

Dialogue
1
0.99750
0.98876

Ortho-phonic0.9855010.98551

difference can be analysed as an inconsistent performance error in manual syllabification. Syllabification in Italian can bemostly predicted algorithmically, even when accounting for minor word boundary segmentation phenomena found in speech.

4. Conclusions
In this paper we present a tool that can be adapted for different tasks, purposes and encodings. The modular structure of the
program is inspired by linguistic principles and constraints. The
syllabification algorithm was reduced to the simplest and mostgeneral mechanism possible, thus demonstrating that it is possible to handle specific cases of Italian syllabification entirely
through changes to the sonority scale. – in real-life applications, this is a crucial function, because the algorithm does notdepend on data, but on structures. As we did for the testing, theporting to other data sets is straightforward and does not require
any tuning of the algorithm itself. Finally, it is possible to use
an instrument that implements a phonological theory as a testing platform for the theory itself, as has been done to prove thepresented SH. A manual verification of the output of the program confirmed the accuracy of the syllabification in a varietyof contexts. Particular clusters arising from re-syllabification
as well as segmentation symbols were handled correctly by theautomatic syllabification.
Possible uses of the program include corpus syllabification,syllable generation and quantitative syllable analysis. In incoming work we will compare an acoustic syllabification algorithm
based on the detection of peaks of intensity in the signal with thetime-aligned phonological syllabification obtained with Sylli.
The difference in the syllabification will eventually help us understand where the signal-based analysis more often fails, thecontexts where the algorithm is less accurate and if these contexts are predictable. In another work the syllabic portions of
the acoustic signal obtained by the syllabification of a timealigned transcription will be extracted automatically and usedin place of segmental units as feeding units for TTS and RS
systems.

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