No abstract

For the past 40 years, lie detection has predominantly been studied in the context of policesuspect and investigative interviews. In their paper, Leach et al. (2016) examined whether niqabs or hijabs interfere with the trial judges' ability to detect deception and concluded that veiling enhanced trial judges' ability to make accurate veracity judgments. In this comment, we argue that the conclusions made by Leach et al. are based upon an inaccurate experimental court paradigm and suffer from methodological and analytical issues. It is our opinion that the applicability of their research findings to real-life court proceedings alongside potential changes to court practices and policies based on Leach et al. should be regarded as na€ ıve and misinformed.

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.