Punctual or Continuous? Analyzing Depression Traces in Language and Paralanguage with Multiple Instance Learning

The key-question addressed in this article is whether the traces of depression in speech are punctual (the pathology manifests itself at specific points in time) or continuous (the pathology manifests itself at every moment in time), where the expression "speech"refers to both speech signals and their transcriptions. For this reason, this work compares the performances of different approaches (both unimodal or multimodal) based either on the assumption that the traces are punctual or on the other one. In this way, it is possible to test which of the alternative assumptions is more realistic. The experiments were performed over a publicly available dataset (the Androids Corpus) and the results include F1 Scores up to 93.1%, among the best reported in literature for the Corpus. Furthermore, the results suggest that depression traces are punctual, but they appear so frequently that approaches based on the assumption of continuous traces still perform well. The conclusions discuss the implications of such observation.