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Depression (major depressive disorder) is one of the most common mental illnesses worldwide, causing feelings of sadness and loss of interest, and is a leading cause of suicidal ideation. Limited access to mental health services, stigma, patient privacy and delay in seeking help are the most significant barriers to assessment and effective treatment. In order to enhance the accuracy of depression prediction, automated strategies employing computational models have been widely explored in literature. To this end, automatic Speech Depression Recognition (SDR) methods stand out, as speech comprises a valuable marker of mental health. Interestingly, recording speech comprises a less intrusive and more portable approach than capturing video, thus more easily accepted, especially by the younger generations, who are at a considerable risk of social isolation due to addiction to social networks and excessive use of mobile devices. In this context, this paper presents an up-to-date survey on SDR. More specifically, we a) detail the major challenges and key issues on SDR, b) summarise the most recent approaches existing in the related literature, and c) highlight the open problems. At the same time, we illustrate a framework encompassing the latest tendencies for SDR, along with a suitable comparison of the achieved performances. Finally, we highlight future trends and present the overall findings, providing researchers with best practices and techniques to address the major challenges of SDR, as well as stimulating discussion and improvement in the field.
Leal et al. (Mon,) studied this question.