Investigation of Speech Landmark Patterns for Depression Detection

Abstract

The massive and growing burden imposed on modern society by depression has motivated investigations into early detection through automated, scalable and non-invasive methods, including those based on speech. However, speech-based methods that capture articulatory information effectively across different recording devices and in naturalistic environments are still needed. This article proposes two feature sets associated with speech articulation events based on counts and durations of sequential landmark groups or n-grams. Statistical analysis of the duration-based features reveals that durations from several consecutive landmark bigrams and onset-offset landmark pairs are significant in discriminating depressed from non-depressed speakers. In addition to investigating different normalization approaches and values of n for landmark n-gram features, experiments across different elicitation tasks suggest that the features can be tailored to capture different articulatory aspects of depressed voices. Evaluations of both landmark duration features and landmark n-gram features on the DAIC-WOZ and SH2 datasets show that they are highly effective, either alone or fused, relative to existing approaches.