Tracing change during music therapy for depression: toward a markers-based understanding of communicative behaviors

This article focuses on behavioral markers—changes in communicative behaviors that reliably indicate the presence and severity of mental health conditions. We explore the potential of behavioral markers to provide new insights and approaches to diagnosis, assessment, and monitoring, with a particular focus on music therapy for depression. We propose a framework for understanding these markers that encompasses three broad functional categories fulfilled by communicative behaviors: semantic, pragmatic, and phatic. The disordered interactions observed in those with depression reflect changes in many types of communicative behavior, but much research has focused on pragmatic behaviors. However, changes in phatic behaviors also seem likely to be important, given their crucial role in facilitating interpersonal relationships. Given the strong phatic element of music-making, music represents a fertile context in which to explore these behaviors. We argue here that the uniquely multimodal and profoundly interactive environment of music therapy in particular allows for the identification of changes in pragmatic and phatic communicative behaviors that reliably indicate depression presence/severity. By identifying these behavioral markers, we open the door to new ways of assessing depression, and improving diagnosis and monitoring. Furthermore, this markers-based approach has broad implications, being applicable beyond depression and beyond music therapy

Speech-based automatic depression detection via biomarkers identification and artificial intelligence approaches

Depression has become one of the most prevalent mental health issues, affecting more than 300 million people all over the world. However, due to factors such as limited medical resources and accessibility to health care, there are still a large number of patients undiagnosed. In addition, the traditional approaches to depression diagnosis have limitations because they are usually time-consuming, and depend on clinical experience that varies across different clinicians. From this perspective, the use of automatic depression detection can make the diagnosis process much faster and more accessible. In this thesis, we present the possibility of using speech for automatic depression detection. This is based on the findings in neuroscience that depressed patients have abnormal cognition mechanisms thus leading to the speech differs from that of healthy people. Therefore, in this thesis, we show two ways of benefiting from automatic depression detection, i.e., identifying speech markers of depression and constructing novel deep learning models to improve detection accuracy. The identification of speech markers tries to capture measurable depression traces left in speech. From this perspective, speech markers such as speech duration, pauses and correlation matrices are proposed. Speech duration and pauses take speech fluency into account, while correlation matrices represent the relationship between acoustic features and aim at capturing psychomotor retardation in depressed patients. Experimental results demonstrate that these proposed markers are effective at improving the performance in recognizing depressed speakers. In addition, such markers show statistically significant differences between depressed patients and non-depressed individuals, which explains the possibility of using these markers for depression detection and further confirms that depression leaves detectable traces in speech. In addition to the above, we propose an attention mechanism, Multi-local Attention (MLA), to emphasize depression-relevant information locally. Then we analyse the effectiveness of MLA on performance and efficiency. According to the experimental results, such a model can significantly improve performance and confidence in the detection while reducing the time required for recognition. Furthermore, we propose Cross-Data Multilevel Attention (CDMA) to emphasize different types of depression-relevant information, i.e., specific to each type of speech and common to both, by using multiple attention mechanisms. Experimental results demonstrate that the proposed model is effective to integrate different types of depression-relevant information in speech, improving the performance significantly for depression detection