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青少年在家庭互动中的言语中临床抑郁的检测。

Detection of clinical depression in adolescents' speech during family interactions.

机构信息

School of Electrical and Computer Engineering, Royal Melbourne Institute of Technology, Vic. 3001, Australia.

出版信息

IEEE Trans Biomed Eng. 2011 Mar;58(3):574-86. doi: 10.1109/TBME.2010.2091640. Epub 2010 Nov 11.

DOI:10.1109/TBME.2010.2091640
PMID:21075715
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3652557/
Abstract

The properties of acoustic speech have previously been investigated as possible cues for depression in adults. However, these studies were restricted to small populations of patients and the speech recordings were made during patients' clinical interviews or fixed-text reading sessions. Symptoms of depression often first appear during adolescence at a time when the voice is changing, in both males and females, suggesting that specific studies of these phenomena in adolescent populations are warranted. This study investigated acoustic correlates of depression in a large sample of 139 adolescents (68 clinically depressed and 71 controls). Speech recordings were made during naturalistic interactions between adolescents and their parents. Prosodic, cepstral, spectral, and glottal features, as well as features derived from the Teager energy operator (TEO), were tested within a binary classification framework. Strong gender differences in classification accuracy were observed. The TEO-based features clearly outperformed all other features and feature combinations, providing classification accuracy ranging between 81%-87% for males and 72%-79% for females. Close, but slightly less accurate, results were obtained by combining glottal features with prosodic and spectral features (67%-69% for males and 70%-75% for females). These findings indicate the importance of nonlinear mechanisms associated with the glottal flow formation as cues for clinical depression.

摘要

先前已有研究调查了语音的声学特性是否可作为成人抑郁的线索。然而,这些研究仅限于小部分患者群体,且语音记录是在患者的临床访谈或固定文本阅读期间进行的。抑郁症状通常首先出现在青春期,此时男女的声音都在发生变化,这表明有必要对青少年群体中的这些现象进行专门研究。本研究在一个由 139 名青少年(68 名临床抑郁患者和 71 名对照者)组成的大样本中调查了抑郁的声学相关因素。在青少年与父母的自然互动期间进行了语音记录。在二元分类框架内测试了韵律、倒谱、频谱和声门特征,以及源自 Teager 能量算子(TEO)的特征。观察到分类准确性存在强烈的性别差异。基于 TEO 的特征明显优于所有其他特征和特征组合,为男性提供了 81%-87%的分类准确性,为女性提供了 72%-79%的分类准确性。将声门特征与韵律和频谱特征相结合可获得更接近但准确性略低的结果(男性为 67%-69%,女性为 70%-75%)。这些发现表明,与声门气流形成相关的非线性机制作为临床抑郁的线索非常重要。

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Dynamics of affective experience and behavior in depressed adolescents.抑郁青少年的情感体验和行为动态。
J Child Psychol Psychiatry. 2009 Nov;50(11):1419-27. doi: 10.1111/j.1469-7610.2009.02148.x. Epub 2009 Aug 21.
2
Role of vortices in voice production: normal versus asymmetric tension.涡流在发声中的作用:正常张力与不对称张力
Laryngoscope. 2009 Jan;119(1):216-21. doi: 10.1002/lary.20026.
3
What can vortices tell us about vocal fold vibration and voice production.涡旋能告诉我们关于声带振动和发声的哪些信息?
基于日常护理中副语言语音特征的机器学习重度抑郁症评估方法的验证
Depress Anxiety. 2024 Apr 9;2024:9667377. doi: 10.1155/2024/9667377. eCollection 2024.
4
Deconstructing demographic bias in speech-based machine learning models for digital health.剖析数字健康领域基于语音的机器学习模型中的人口统计学偏差
Front Digit Health. 2024 Jul 25;6:1351637. doi: 10.3389/fdgth.2024.1351637. eCollection 2024.
5
Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization.基于声门激励能量和频谱特征融合及特征优化的应激语音情感识别
Comput Intell Neurosci. 2023 Oct 11;2023:5765760. doi: 10.1155/2023/5765760. eCollection 2023.
6
Fast and accurate assessment of depression based on voice acoustic features: a cross-sectional and longitudinal study.基于语音声学特征的抑郁症快速准确评估:一项横断面和纵向研究。
Front Psychiatry. 2023 Jun 21;14:1195276. doi: 10.3389/fpsyt.2023.1195276. eCollection 2023.
7
Multidimensional voiceprint feature assessment system for identifying the depression in children and adolescents: a diagnostic test.用于识别儿童和青少年抑郁症的多维声纹特征评估系统:一项诊断试验
Front Psychiatry. 2023 May 10;14:1105534. doi: 10.3389/fpsyt.2023.1105534. eCollection 2023.
8
Estimating Depressive Symptom Class from Voice.从声音估计抑郁症状类别。
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9
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Diagnostics (Basel). 2022 Nov 17;12(11):2844. doi: 10.3390/diagnostics12112844.
10
The applicability of the Beck Depression Inventory and Hamilton Depression Scale in the automatic recognition of depression based on speech signal processing.贝克抑郁量表和汉密尔顿抑郁量表在基于语音信号处理的抑郁症自动识别中的适用性。
Front Psychiatry. 2022 Aug 4;13:879896. doi: 10.3389/fpsyt.2022.879896. eCollection 2022.
Curr Opin Otolaryngol Head Neck Surg. 2008 Jun;16(3):183-7. doi: 10.1097/MOO.0b013e3282ff5fc5.
4
Etiology of depression and implications on work environment.抑郁症的病因及其对工作环境的影响。
J Occup Environ Med. 2008 Apr;50(4):391-5. doi: 10.1097/JOM.0b013e31816fca08.
5
The frequency of perceived stress, anxiety, and depression in patients with common pathologies affecting voice.影响嗓音的常见病症患者感知到的压力、焦虑和抑郁的发生率。
J Voice. 2008 Jul;22(4):472-88. doi: 10.1016/j.jvoice.2006.08.007. Epub 2008 Apr 18.
6
TKK Aparat: an environment for voice inverse filtering and parameterization.TKK设备:一种用于语音逆滤波和参数化的环境。
Logoped Phoniatr Vocol. 2008;33(1):49-64. doi: 10.1080/14015430701855333.
7
Critical analysis of the impact of glottal features in the classification of clinical depression in speech.声门特征对语音中临床抑郁症分类影响的批判性分析。
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8
Formal psychological testing in patients with paradoxical vocal fold dysfunction.矛盾性声带功能障碍患者的正式心理测试。
Laryngoscope. 2008 Apr;118(4):740-7. doi: 10.1097/MLG.0b013e31815ed13a.
9
Vortical flow field during phonation in an excised canine larynx model.切除的犬喉模型发声时的涡流场
Ann Otol Rhinol Laryngol. 2007 Mar;116(3):217-28. doi: 10.1177/000348940711600310.
10
Telephony-based voice pathology assessment using automated speech analysis.基于电话的语音病理学评估:使用自动语音分析
IEEE Trans Biomed Eng. 2006 Mar;53(3):468-77. doi: 10.1109/TBME.2005.869776.