Abstract
Depression is a frequent affective disorder, leading to a high impact on patients, their families and society. Depression diagnosis is limited by assessment methods that rely on patient-reported or clinician judgments of symptom severity. Recently, many researches showed that voice is an objective indicator for depressive diagnosis. In this paper, we investigate a sample of 111 subjects (38 healthy controls, 36 mild depressed patients and 37 severe depressed patients) through comparative analysis to explore the correlation between acoustic features and depression severity. We extract features as many as possible according to previous researches to create a large voice feature set. Then we employ some feature selection methods to form compact subsets on different tasks. Finally, we evaluate depressive disorder severity by these acoustic feature subsets. Results show that interview is a better choice than reading and picture description for depression assessment. Meanwhile, speech signal correlate to depression severity in a medium-level with statistically significant (p < 0.01).
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Acknowledgment
This work was supported by the National Basic Research Program of China (973 Program) (No. 2014CB744600), the Program of International S&T Cooperation of MOST (No. 2013DFA11140), the National Natural Science Foundation of China (grant No. 61210010, No. 61300231). Grateful acknowledgement is made to my classmates: Xiang Gao, Jinning Zhao, Xin Guo, Fei Heng and Lele He. They gave us considerable help by means of data collection, comments and criticism.
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Liu, Z. et al. (2016). Evaluation of Depression Severity in Speech. In: Ascoli, G., Hawrylycz, M., Ali, H., Khazanchi, D., Shi, Y. (eds) Brain Informatics and Health. BIH 2016. Lecture Notes in Computer Science(), vol 9919. Springer, Cham. https://doi.org/10.1007/978-3-319-47103-7_31
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DOI: https://doi.org/10.1007/978-3-319-47103-7_31
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