China
Research Article
Detecting Depression in Speech: A Multi-classifier System with Ensemble Pruning on Kappa-Error Diagram
Author(s): Hailiang Long, Xia Wu, Zhenghao Guo, Jianhong Liu and Bin HuHailiang Long, Xia Wu, Zhenghao Guo, Jianhong Liu and Bin Hu
Depression is a severe mental health disorder with high societal costs. Despite its high prevalence, its diagnostic rate is very low. To assist clinicians to better diagnose depression, researchers in recent years have been looking at the problem of automatic detection of depression from speech signals. In this study, a novel multi-classifier system for depression detection in speech was developed and tested. We collected speech data in different ways, and we examined the discriminative power of different speech types (such as reading, interview, picture description, and video description). Considering that different speech types may elicit different levels of cognitive effort and provide complementary information for the classification of depression, we can utilize various speech data sets to gain a better result for depression recognition. All individual learners formed a pool of cl.. Read More»
DOI:
10.4172/2157-7420.1000293
Journal of Health & Medical Informatics received 2700 citations as per Google Scholar report