The Detection of Depression Using Multimodal Models Based on Text and Voice Quality Features

The article proves the concept that an automatic diagnosis of depression can be achieved using audio recordings of the individuals' voices. DAIC-WOZ database was used as a data source. Audio and textual data were preprocessed and converted to a set of optimized parameters for two models. Appropriate Deep Learning models to detect depression in the transcripts of the audio recordings and voice quality features, were utilized. We created a text analysis model on a word-level using Natural Language Processing (NLP) techniques, and a voice quality analysis model on tense to breathy dimension. The text analysis model made its best performance with an F1-score equal to 0.8 (0.42) for non-depressed (depressed) individuals, while the voice quality model scored 0.76 (0.38). As a result, we had two models that would be implemented in a system for the diagnosis of depression.