KIParla Corpus: A New Resource for Spoken Italian
Authors: Caterina Mauri, Silvia Ballarè, Eugenio Goria, Massimo Cerruti, Francesco Suriano (Università di Bologna, Università di Torino, 2019)
Topic: Multimodal

Abstract
In this paper we introduce the main features of the KIParla corpus, a new resource for the study of spoken Italian. In addition to its other capabilities, KIParla provides access to a wide range of metadata that characterize both the participants and the settings in which the interactions take place. Furthermore, it is designed to be shared as a free resource tool through the NoSketch Engine interface and to be expanded as a monitor corpus.

Introduction
The aim of this paper is to describe the design and implementation of a new resource tool for the study of spoken Italian. The KIParla corpus is the result of a joint collaboration between the Universities of Bologna and Turin and is open to further partnerships in the future. It is characterized by a number of innovative features. In addition to providing access to a wide range of metadata concerning the speakers and the setting in which the interactions take place, it offers transcriptions time-aligned with audio files and is designed to be expanded and upgraded through the addition of independent modules, constructed with a similar attention to the metadata; moreover, it is completely open-access and makes use of open-access technologies, such as the NoSketch Engine platform. Section 2 provides a detailed description of the corpus design, aimed at featuring the geographic, social and situational variation that characterizes spoken Italian. In Section 3 we discuss corpus implementation, describing how data have been collected in adherence with ethical requirements, how they have been treated and transcribed, and how they have been made accessible and searchable through NoSketch Engine. Section 4 focuses on the incremental modularity of the corpus, which makes it an open monitor corpus of spoken Italian. The two modules that constitute the current core of KIParla, namely KIP and ParlaTO, are then briefly illustrated, and some prospects for future developments are outlined.

Conclusion
The ParlaTO corpus has been added to the KIP corpus, thereby creating two modules within the larger KIParla corpus. We aim to make this resource grow over time through subsequent additions and upgrades. The leading idea is that the greater the variety of interactions, speakers, and geographical areas recorded in the KIParla data, the more the corpus will become representative of the language(s) and language varieties spoken in Italy. Moreover, as the corpus is upgraded over time, it will tell us more and more about the sociolinguistic situation in the Italian peninsula. We envision the future development of the corpus to proceed in two main directions. On the one hand, we intend to collaborate with existing projects, in order to verify whether data already collected for different purposes may be adapted into new modules of the KIParla corpus. The only requirement in such cases is the ability to trace and access a core set of metadata for the speakers (gender, age, geographical information, level of education, and occupation) and for the interaction (interview, free conversation, etc.). Further metadata would of course be welcome. Moreover, new data collection efforts have already started or are scheduled to start in different regions (e.g. in Lombardy). A data collection project parallel to ParlaTO is also planned for Bologna. The second direction along which KIParla will grow has to do with data annotation. For the moment, KIParla data are available as prosodic and orthographic transcriptions, time-aligned with the speech audio file and linked to the metadata of speakers and interactions. Further functions are offered by NoSketch Engine, such as word sketches, thesaurus, and keyword computation. We plan two further stages of annotation, namely lemmatization and POS-tagging, which will significantly enhance data retrieval. Due to space constraints, we are unable to discuss the problems that lemmatization and POS-tagging raise when applied to spoken data (cf. Panunzi, Picchi, Moneglia 2004), and leave such a crucial discussion to future work.