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Alternative TitleAn Exploratory Study on Auxiliary Diagnosis of Depression Based on Speech
Thesis Advisor朱廷劭
Degree Grantor中国科学院研究生院
Place of Conferral北京
Degree Discipline应用心理学
Keyword抑郁症 辅助诊断 语音 分类 预测


Other Abstract

Major depression disorder (MDD) is a kind of mental illness which is accompany with a core symptom of depressive mood and various other symptoms. To improve the diagnostic effect, we are eager to find an objective indicator that could effectively reflect the real status of depression. Speech is one kind of easily available behavioral clue. Many studies indicated that there are significant correlations between acoustic features and depressive symptoms, and it is possible to identify depression via acoustic features. Nevertheless, there are still two questions need to be figured out: how about the diagnostic power of speech when used it to identify depression? Are the predictive effects of speech cross-situational consistency?
To figure out the question about diagnostic power of speech, we designed a series of experiments to identify depression for finding out the utmost of identification in our case. In our studies, we applied classification algorithms of machine learning to analyze the predictive effects of acoustic features, and used metrics like F-measure to estimate the predictive models. The question about cross-situational consistency of speech’ predictive effects was investigated by comparing predictive effects under different experimental situations, tasks and emotions.
The main aim of study 1 is discriminate depressed people from non-mental patients. Study 1 includes three experiments. Experiment 1 compared the subjective emotional experiences among depressed, bipolar and healthy people, the results suggested that it is hard to distinguish these three groups while used self-report emotions only. Experiment 2 found that acoustic features can be used to differentiate depression and healthy people, the F-measure was reach 78.2%. Besides, the predictive effects can reach moderate levels under different situations. Experiment 3 indicated that acoustic features can be applied to distinguish depressed people and physical patients, the best F-measure was 75.6%.
The main aim of study 2 is distinguish depressed people from other kinds of mental patients. Study 2 includes two experiments. Experiment 4 shown that acoustic features can be applied to differentiate depressed people with one kind of comorbidity and depressed people without this comorbidity, the F-measure reached 75%. The goal of experiment 5 is to distinguish depression from bipolar disorder. The results reported:
(1) acoustic features can be used to differentiate depression and bipolar disorder, the best F-measure was 80.9%; (2) acoustic identification was able to discriminate healthy, bipolar and depressed people, the best F-measure was 60.8%; (3) the predictive effects of acoustic features can reach moderate levels under different situations.
This paper explores the diagnostic power of speech, and find that depression could be effectively identified from the mixture crowd of depressed and bipolar people. And this diagnostic power has cross-situational consistency. In the future studies, researchers should try to identify different degrees of MDD or subtypes within MDD in a larger sample.

Document Type学位论文
First Author AffilicationInstitute of Psychology, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
汪静莹. 抑郁症的辅助诊断研究——基于语音特征的探索[D]. 北京. 中国科学院研究生院,2017.
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