Mental Wellbeing at Sea: Active and Passive Speech Monitoring in a Maritime Setting

The mental wellbeing of seafarers is particularly at risk due to the harsh conditions of their work environment. Speech is a promising modality to assess mental wellbeing in an unobtrusive way. In this study, we outline our approach to collect speech data in the challenging context of an oil tanker to monitor the mental wellbeing of a ship’s crew. In the course of this study, we employed two data collection approaches: 1) “active data collection” through the local (offline) deployment of a web-based platform, which participants accessed through their end devices, and 2) “passive data collection” through the ship’s voyage data recorder (VDR) system from 4 microphones on the bridge and 2 radio communication channels. For the active data, we analysed speech features and self-reported wellbeing measures. For the passive data, we applied denoising, segmented speech, and predicted dimensional emotional expression (valence, arousal, dominance). We incorporated environmental variables such as absolute wind speed and operational context (at sea, in port, cargo operations). We gathered speech data from 25 seafarers over a nine-week period, resulting in 378 actively recorded survey sessions. Analysis of the actively collected data reveals moderate correlations (r 0.3−0.4) between speech features and self-reported wellbeing measures. In the passively collected data, higher wind speeds were associated with lower inferred emotional intensity on the ship’s bridge microphones, particularly at sea (r −0.34). A mediation analysis showed that this ship’s bridge emotion intensity partially mediates the association between wind and concurrent self-assessed stress. Denoising improved signal-to-noise ratio distributions but had only modest effects on predictive performance. Our findings demonstrate the technical feasibility of combined active and passive speech monitoring in maritime settings and provide exploratory evidence for environmental influences on crew affective states. This work lays the foundation for future studies on personalised modelling and large-scale passive monitoring to support seafarer mental health.