Depresyon şiddeti değerlendirmesi için konuşma çevriyazılarının modellenmesi

It is true that everyone has bad days from time to time. Unfortunately, for people suffering from depression, every day is a constant battle for motivation to do even the simplest of things, all the while dealing with hopelessness, physical and emotional fatigue, and sadness. Considering the ever increasing number of people suffering from this disease, the necessity for automated depression severity assessment systems is profound. These systems can be used in treatment procedures, and the findings provided from learned models can help us better understand the dynamics of depression. To help in the solution to this illness, we propose a modular deep learning pipeline that uses speech transcripts as input for depression severity prediction. Through our pipeline, we investigate the role of popular deep learning architectures in creating representations for depression assessment. To extend the depression assessment literature on text modality, we provide a thorough analysis of sentence statistics and their effects on model training. We also present an investigation regarding the use of sentiment information for depression assessment. Evaluation of the proposed architectures is performed on the publicly available Extended Distress Analysis Interview Corpus dataset (E-DAIC). Through the results and discussions, we show that informative representations for depression assessment can be obtained without exploiting the temporal dynamics between sentences. Our proposed non-temporal model outperforms the state of the art by %8.8 in terms of Concordance Correlation Coefficient (CCC). In light of our findings on trained models and data statistics, we discuss how recurrent structures can have a bias toward certain sequence lengths during training and that shorter.