Using i-vectors from voice features to identify major depressive disorder | |
Di, Yazheng1,2; Wang, Jingying3; Li, Weidong4; Zhu, Tingshao1,2 | |
刊名 | JOURNAL OF AFFECTIVE DISORDERS |
2021-06-01 | |
卷号 | 288页码:161-166 |
关键词 | Depression Biological markers Clinical trials Computer internet technology Assessment Diagnosis |
ISSN号 | 0165-0327 |
DOI | 10.1016/j.jad.2021.04.004 |
产权排序 | 1 |
文献子类 | 实证研究 |
英文摘要 | Background: Machine-learning methods using acoustic features in the diagnosis of major depressive disorder (MDD) have insufficient evidence from large-scale samples and clinical trials. This study aimed to evaluate the effectiveness of the promising i-vector method on a large sample of women with recurrent MDD diagnosed clinically, examine its robustness, and provide an explicit acoustic explanation of the i-vectors. Methods: We collected utterances edited from clinical interview speech records of 785 depressed and 1,023 healthy individuals. Then, we extracted Mel-frequency cepstral coefficient (MFCC) features and MFCC i-vectors from their utterances. To examine the effectiveness of i-vectors, we compared the performance of binary logistic regression between MFCC i-vectors and MFCC features and tested its robustness on different utterance durations. We also determined the correlation between MFCC features and MFCC i-vectors to analyze the acoustic meaning of i-vectors. Results: The i-vectors improved 7% and 14% of area under the curve (AUC) for MFCC features using different utterances. When the duration is > 40 s, the classification results are stabilized. The i-vectors are consistently correlated to the maximum, minimum, and deviations of MFCC features (either positively or negatively). Limitations: This study included only women. Conclusions: The i-vectors can improve 14% of the AUC on a large-scale clinical sample. This system is robust to utterance duration > 40 s. This study provides a foundation for exploring the clinical application of voice features in the diagnosis of MDD. |
WOS关键词 | MENTAL-DISORDERS ; EPIDEMIOLOGY ; SYMPTOMS ; DISEASE ; RISK ; CARE |
WOS研究方向 | Neurosciences & Neurology ; Psychiatry |
出版者 | ELSEVIER |
WOS记录号 | WOS:000655555400022 |
内容类型 | 期刊论文 |
源URL | [http://ir.psych.ac.cn/handle/311026/39472] |
专题 | 心理研究所_中国科学院行为科学重点实验室 |
通讯作者 | Li, Weidong; Zhu, Tingshao |
作者单位 | 1.Inst Psychol, CAS Key Lab Behav Sci, 16 Lincui Rd, Beijing 100101, Peoples R China 2.Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China 3.Hong Kong Polytech Univ, Sch Optometry, Hung Hom, Kowloon, Hong Kong, Peoples R China 4.Shanghai Jiao Tong Univ, 800 Dongchuan Rd, Shanghai 200240, Peoples R China |
推荐引用方式 GB/T 7714 | Di, Yazheng,Wang, Jingying,Li, Weidong,et al. Using i-vectors from voice features to identify major depressive disorder[J]. JOURNAL OF AFFECTIVE DISORDERS,2021,288:161-166. |
APA | Di, Yazheng,Wang, Jingying,Li, Weidong,&Zhu, Tingshao.(2021).Using i-vectors from voice features to identify major depressive disorder.JOURNAL OF AFFECTIVE DISORDERS,288,161-166. |
MLA | Di, Yazheng,et al."Using i-vectors from voice features to identify major depressive disorder".JOURNAL OF AFFECTIVE DISORDERS 288(2021):161-166. |
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