Managing, storing, and sharing long-form recordings and their annotations. - Gautheron, Lucas; Rochat, Nicolas; Cristia, Alejandrina (Language Resources & Evaluation, 2023)
The technique of long-form recordings via wearables is gaining momentum in different fields of research, notably linguistics and neurology. This technique, however, poses several technical challenges, some of which are amplified by the peculiarities of the data, including their sensitivity and their volume. In this paper, we begin by outlining key problems related to the management, storage, and sharing of the corpora that emerge when using this technique. We continue by proposing a multi-component solution to these problems, specifically in the case of daylong recordings of children. As part of this solution, we release ChildProject, a Python package for performing the operations typically required by such datasets and for evaluating the reliability of annotations using a number of measures commonly used in speech processing and linguistics. This package builds upon an annotation management system, which allows the importation of annotations from a wide range of existing formats, as well as upon data validation procedures, which assert the conformity of the data, or, alternatively, produce detailed and explicit error reports. Our proposal could be generalized to populations other than children and beyond linguistics.