A generic tool for the automatic syllabification of Italian
Authors: Brigitte Bigi, Caterina Petrone (CNRS, Aix-Marseille Université, 2014)
Topic: Multimodal

Abstract
This paper presents a rule-based automatic syllabification for Italian. Dif-ferently from previously proposed syllab-ifiers, our approach is more user-friendly since the Python algorithm includes both a Command-Line User and a Graphical User interfaces. Moreover, phonemes, classes and rules are listed in an external configuration file of the tool which can be easily modified by any user. Syllabifica-tion performance is consistent with man-ual annotation. This algorithm is included in SPPAS, a software for automatic speech segmentation, and distributed under the terms of the GPL license.

Introduction
This paper presents an approach to automatic de-tection of syllable boundaries for Italian speech. This syllabifier makes use of the phonetized text. The syllable is credited as a linguistic unit con-ditioning both segmental (e.g., consonant or vowel lengthening) and prosodic phonology (e.g., tune-text association, rhythmical alternations) and its automatic annotation represent a valuable tool for quantitative analyses of large speech data sets. While the phonological structure of the syllable is similar across different languages, phonolog-ical and phonotactic rules of syllabification are language-specific. Automatic approaches to syl-lable detection have thus to incorporate such con-straints to precisely locate syllable boundaries. The question then arises of how to obtain an ac-ceptable syllabification for a particular language and for a specific corpus (a list of words, a writ-ten text or an oral corpus of more or less casual speech). In the state-of-the-art, the syllabification can be made directly from a text file as in (Cioni, 1997), or directly from the speech signal as in (Petrillo and Cutugno, 2003). There are two broad approaches to the prob-lem of the automatic syllabification: a rule-based approach and a data-driven approach. The rule-based method effectively embodies some theoret-ical position regarding the syllable, whereas the data-driven paradigm tries to infer new syllabifica-tions from examples syllabified by human experts. In (Adsett et al., 2009), three rule-based automatic systems and two data-driven automatic systems (Syllabification by Analogy and the Look-Up Pro-cedure) are compared to syllabify a lexicon. Indeed, (Cioni, 1997) proposed an algorithm for the syllabification of written texts in Italian, by syllabifying words directly from a text. It is an algorithm of deterministic type and it is based upon the use of recursion and of binary tree in order to detect the boundaries of the syllables within each word. The outcome of the algorithm is the production of the so-called canonical syllabification (the stream of syllabified words). On the other side, (Petrillo and Cutugno, 2003) presented an algorithm for speech syllabification directly using the audio signal for both English and Italian. The algorithm is based on the detection of the most relevant energy maxima, using two different energy calculations: the former from the original signal, the latter from a low-pass filtered version. This method allows to perform the syllabification with the audio signal only, so without any lexical information. More recently, (Iacoponi and Savy, 2011) developed a complete rule-based syllabifier for Italian (named Sylli) that works on phonemic texts. The rules are then based on phonological principles. The system is composed of two transducers (one for the input and one for the output), the syllabification algorithm and the mapping list (i.e., the vocabulary). The two transducers convert the twodimensional 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 a linear string for output printing. The system achieved good performances compared to a manual syllabification: more than 0.98.5% (syllabification of spoken words). This system is distributed as a package written in C language and must be compiled; the program is an interactive test program that is used in command-line mode. After the program reads in the phone set definition and syllable structure parameters, it loops asking for the user to type in a phonetic transcription, calculating syllable boundaries for it, and then displaying them. When the user types in a null string, the cycling stops and execution ends. Finally, there are two main limitations: this tool is only dedicated to computer scientists, and it does not support time-aligned input data. With respect to these already existing approaches and/or systems, the novel aspect of the work reported in this paper is as follows: to propose a generic and easy-to-use tool to identify syllabic segments from phonemes; to propose a generic algorithm, then a set of rules for the particular context of Italian spontaneous speech. In this context, ”generic” means that the phone set, the classes and the rules are easily changeable; and ”easy-to-use” means that the system can be used by any user.

Conclusion
The paper presented a new feature of the SPPAS tool that lets the user provide syllabification rules and perform automatic segmentation by means of a well-designed graphical user interface. The sys-tem is mainly dedicated to linguists that would like to design and test their own set of rules. A man-ual verification of the output of the program con-firmed the accuracy of the proposed set of rules for syllabification of dialogues. Furthermore, the rules or the list of phonemes can be easily modi-fied by any user. Possible uses of the program in-clude speech corpus syllabification, dictionary syl-labification, and quantitative syllable analysis.