Listening to Stress: A Multimodal Approach towards Analysis of Mindfulness Meditation Effects

Abstract

Stress monitoring and management have become increasingly critical owing to the rising prevalence of mental health challenges in contemporary society. Mindfulness meditation has demonstrated potential as an effective intervention. However, objective and scalable frameworks for evaluating its impact remain limited. This study presents a multimodal approach for stress assessment that integrates speech-based biomarkers, psychological measures, and cognitive performance indicators to evaluate the effectiveness of guided mindfulness meditation. A web-based platform was developed to facilitate automated data collection and longitudinal monitoring. It incorporated speech recordings, Perceived Stress Scale (PSS) responses, and cognitive assessments, including reaction-time and Stroop color-word tasks. Acoustic features such as fundamental frequency, Mel-frequency cepstral coefficients (MFCCs), and local jitter were extracted from speech signals to investigate their relationship with stress levels. Results show consistent reductions in stress indicators, accompanied by improved behavioral performance and decreased self-reported stress. Speech-derived features exhibit strong potential as complementary biomarkers. The proposed multimodal framework supports objective, scalable, and non-invasive stress assessment.