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Managing, storing, and sharing long-form recordings and their annotations.

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...

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Published in:Language Resources & Evaluation Vol. 57; no. 1; pp. 343 - 376
Main Authors: Gautheron, Lucas, Rochat, Nicolas, Cristia, Alejandrina
Format: Article
Published: Springer Nature Mar2023
Subjects:
Annotations
Speech
Sharing
Cloud storage
Online Access:View this record in EBSCOhost
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        10.1007/s10579-022-09579-3
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        atl: Managing, storing, and sharing long-form recordings and their annotations.
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          Gautheron, Lucas
          Rochat, Nicolas
          Cristia, Alejandrina
        affil: Laboratoire de Sciences Cognitives et de Psycholinguistique, Département d'Etudes cognitives, ENS, EHESS, CNRS, PSL University, Paris, France
      su:
        Annotations
        Speech
        Sharing
        Cloud storage
      sug:
        subj:
          Annotations
          Speech
          Sharing
          Cloud storage
      keyword:
        Annotation evaluation
        Data distribution
        Daylong recordings
        Inter-rater reliability
        Reproducible research
        Speech data management
      ab: 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.
      pubtype: Academic Journal
      doctype: Article
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    language: English
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      custom: Language Resources & Evaluation is a copyright of Springer, 2023. All Rights Reserved.
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