Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features
文献类型：期刊论文
| 作者 | Luo, Qian1,2; Di, Yazheng1,2; Zhu, Tingshao1
|
| 刊名 | JOURNAL OF AFFECTIVE DISORDERS
 |
| 出版日期 | 2024-05-01 |
| 卷号 | 352页码:395-402 |
| 关键词 | Neuroticism Depression Clinical trials Computer/internet technology Assessment/diagnosis |
| ISSN号 | 0165-0327 |
| DOI | 10.1016/j.jad.2024.02.021 |
| 通讯作者 | Zhu, Tingshao(tszhu@psych.ac.cn) |
| 英文摘要 | Background: Neuroticism's impact on psychopathological and physical health issues has significant public health implications. Multiple studies confirm its predictive effect on suicide risk among depressed patients. However, previous research lacks a standardized criterion for assessing neuroticism through speech, often relying on simple features (such as pitch, loudness and MFCCs). This study aims to improve upon this by extracting features using advanced pre -trained speaker embedding models (i-vector and x -vector extractors). Additionally, unlike prior studies utilizing general population data, we explore neuroticism prediction in depressed and non -depressed subgroups. Methods: We collected edited discourse data from clinical interviews of 3580 depressed individuals and 4016 healthy individuals from the CONVERGE study. Instead of solely extracting Low -Level Acoustic Descriptors, we incorporated i-vector and x -vector features. We compared the performance of three different features in predicting neuroticism and explored their combination to enhance model accuracy. Results: The SVR model, combining three speech features with downscaled features to 300, exhibited the highest performance in predicting neuroticism scores. It achieved a coefficient of determination (R -squared) of 0.3 or higher and a correlation of 0.56 between predicted and actual values. The predictive classification accuracy of speech features for neuroticism in specific populations (healthy and depressed) exceeded 60 %. Limitations: This study included only women. Conclusion: Combining diverse speech features enhances the predictive capacity of models using speech features to assess neuroticism, particularly in specific populations. This study lays the foundation for future exploration of speech features in neuroticism prediction. |
| 收录类别 | SCI |
| WOS关键词 | PERSONALITY ; SPEECH ; VALIDATION ; DISORDERS ; VECTORS |
| 资助项目 | Chinese Academy of Sciences[KJZD-SW-L10] |
| WOS研究方向 | Neurosciences & Neurology ; Psychiatry |
| 语种 | 英语 |
| WOS记录号 | WOS:001202669200001 |
| 出版者 | ELSEVIER |
| 资助机构 | Chinese Academy of Sciences |
| 源URL | [http://ir.psych.ac.cn/handle/311026/47616]  |
| 专题 | 心理研究所_社会与工程心理学研究室 |
| 通讯作者 | Zhu, Tingshao |
| 作者单位 | 1.Chinese Acad Sci, Inst Psychol, Beijing 100101, Peoples R China 2.Univ Chinese Acad Sci, Dept Psychol, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Luo, Qian,Di, Yazheng,Zhu, Tingshao. Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features[J]. JOURNAL OF AFFECTIVE DISORDERS,2024,352:395-402. |
| APA | Luo, Qian,Di, Yazheng,&Zhu, Tingshao.(2024).Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features.JOURNAL OF AFFECTIVE DISORDERS,352,395-402. |
| MLA | Luo, Qian,et al."Predictive modeling of neuroticism in depressed and non-depressed cohorts using voice features".JOURNAL OF AFFECTIVE DISORDERS 352(2024):395-402. |
入库方式： OAI收割
来源：心理研究所
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