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Depression is a common mental health problem leading to significant disability worldwide. It is not only common but also commonly co-occurs with other mental and neurological illnesses. Parkinson’s disease (PD) gives rise to symptoms directly impairing a person’s ability to function. Early diagnosis and detection of depression can aid in treatment, but diagnosis typically requires an interview with a health provider or a structured diagnostic questionnaire. Thus, unobtrusive measures to monitor depression symptoms in daily life could have great utility in screening depression for clinical treatment. Vocal biomarkers of depression are a potentially effective method of assessing depression symptoms in daily life, which is the focus of the current research. We have a database of 921 unique PD patients and their self-assessment of whether they felt depressed or not. Voice recordings from these patients were used to extract paralinguistic features, which served as inputs to machine learning and deep learning techniques to predict depression. The results are presented here, and the limitations are discussed given the nature of the recordings which lack language content. Our models achieved accuracies as high as 0.77 in classifying depressed and nondepressed subjects accurately using their voice features and PD severity. We found depression and severity of PD had a correlation coefficient of 0.3936, providing a valuable feature when predicting depression from voice. Our results indicate a clear correlation between feeling depressed and PD severity. Voice may be an effective digital biomarker to screen for depression among PD patients.

Depression is a widespread mental health problem around the world with a significant burden on economies. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use technology and monitor depression remotely and relatively inexpensively using automated agents. There has been numerous efforts to automatically assess depression levels using audiovisual features as well as text-analysis of conversational speech transcriptions. However, difficulty in data collection and the limited amounts of data available for research present challenges that are hampering the success of the algorithms. One of the two novel contributions in this paper is to exploit databases from multiple languages for acoustic feature selection. Since a large number of features can be extracted from speech, given the small amounts of training data available, effective data selection is critical for success. Our proposed multi-lingual method was effective at selecting better features than the baseline algorithms, which significantly improved the depression assessment accuracy. The second contribution of the paper is to extract text-based features for depression assessment and use a novel algorithm to fuse the text- and speech-based classifiers which further boosted the performance.

Depression is a common mental health problem around the world with a large burden on economies, well-being, hence productivity, of individuals. Its early diagnosis and treatment are critical to reduce the costs and even save lives. One key aspect to achieve that goal is to use voice technologies and monitor depression remotely and relatively inexpensively using automated agents. Although there has been efforts to automatically assess depression levels from audiovisual features, use of transcriptions along with the acoustic features has emerged as a more recent research venue. Moreover, difficulty in data collection and the limited amounts of data available for research are also challenges that are hampering the success of the algorithms. One of the novel contributions in this paper is to exploit the databases from multiple languages for feature selection. Since a large number of features can be extracted from speech, and given the small amounts of training data available, effective data selection is critical for success. Our proposed multilingual method was effective at selecting better features and significantly improved the depression assessment accuracy. We also use textbased features for assessment and propose a novel strategy to fuse the text-and speech-based classifiers which further boosted the performance.