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Background: Computerized speech analysis (CSA) is a powerful method that allows one to assess stress-induced mood disturbances and affective disorders through repeated measurements of speaking behavior and voice sound characteristics. Over the past decades CSA has been successfully used in the clinical context to monitor the transition from ‘affectively disturbed' to ‘normal' among psychiatric patients under treatment. This project, by contrast, aimed to extend the CSA method in such a way that the transition from ‘normal' to ‘affected' can be detected among subjects of the general population through 10-20 self-assessments. Methods: Central to the project was a normative speech study of 5 major languages (English, French, German, Italian, and Spanish). Each language comprised 120 subjects stratified according to gender, age, and education with repeated assessments at 14-day intervals (total n = 697). In a first step, we developed a multivariate model to assess affective state and stress-induced bodily reactions through speaking behavior and voice sound characteristics. Secondly, we determined language-, gender-, and age-specific thresholds that draw a line between ‘natural fluctuations' and ‘significant changes'. Thirdly, we implemented the model along with the underlying methods and normative data in a self-assessment ‘voice app' for laptops, tablets, and smartphones. Finally, a longitudinal self-assessment study of 36 subjects was carried out over 14 days to test the performance of the CSA method in home environments. Results: The data showed that speaking behavior and voice sound characteristics can be quantified in a reproducible and language-independent way. Gender and age explained 15-35% of the observed variance, whereas the educational level had a relatively small effect in the range of 1-3%. The self-assessment ‘voice app' was realized in modular form so that additional languages can simply be ‘plugged in' once the respective normative data become available. Results of the longitudinal self-assessment study in home environments demonstrated that CSA methods work well under most circumstances. Conclusions: We have successfully developed and tested a self-assessment CSA method that can monitor transitions from ‘normal' to ‘affected' in subjects of the general population in the broader context of mood disorders. Our easy-to-use ‘voice app' evaluates sequences of 10-20 repeated assessments and watches for affect- and stress-induced deviations from baseline that exceed language-, gender-, and age-specific thresholds. Specifically, the ‘voice app' provides users with stress-related ‘biofeedback' and can help to identify that 10-15% subgroup of the general population that exhibits insufficient coping skills under chronic stress and may benefit from early detection and intervention prior to developing clinically relevant symptoms.