Computational Analysis of Voice as Digital Biomarkers for Clinical Assessment for Distress in Female Cancer Patients

Digital biomarkers offer novel approaches to non-invasive health monitoring, particularly in Palliative Care, for example in cancer patients, where the identification and relief of symptom burden and distress are the leading goals of care. This study investigates the correlation between acoustic speech features and distress severity in female cancer patients, using the Edmonton Symptom Assessment System (ESAS) as a reference. Speech recordings were collected from 28 cancer patients at up to four different time points, with acoustic features extracted using the openSMILE toolkit (ComParE 2016 feature set). The analysis focused on the 23rd and 24th Mel filter-bank bands-MFB 23 and MFB 24-which are two individual channels of the 26-channel Mel filter-bank computed by openSMILE. Pearson correlation analysis identified 13 spectral features significantly associated with the ESAS score of the female cohort. A multivariate Ordinary Least Squares (OLS) regression model demonstrated that selected acoustic parameters explained 29% of the variance in distress levels, with spectral flatness and mean energy in MFB 23 emerging as key predictors. These findings suggest that speech-based biomarkers may facilitate automated, objective distress screening in oncology patients. By integrating acoustic analysis into clinical workflows, this study highlights the potential of digital voice biomarkers for continuous symptom monitoring. Future research should refine predictive models and expand patient cohorts to enhance clinical applicability.Clinical relevance-This study highlights the potential of speech-derived digital biomarkers for distress screening in female palliative oncology patients. By correlating acoustic speech features with ESAS scores, it demonstrates a noninvasive, objective method for symptom monitoring. Integrating voice analysis into clinical workflows could enhance early intervention, reduce patient burden, and improve precision in symptom management, for example by integration into telemedicine interventions and follow-ups.