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本文引用的文献

1
Detecting Depression Severity from Vocal Prosody.从嗓音韵律中检测抑郁症严重程度。
IEEE Trans Affect Comput. 2013 Apr-Jun;4(2):142-150. doi: 10.1109/T-AFFC.2012.38. Epub 2013 Jul 11.
2
Treatment and Prevention of Depression.抑郁症的治疗与预防。
Psychol Sci Public Interest. 2002 Nov;3(2):39-77. doi: 10.1111/1529-1006.00008. Epub 2002 Nov 1.
3
Nonverbal Social Withdrawal in Depression: Evidence from manual and automatic analysis.抑郁症中的非言语社交退缩:来自手动和自动分析的证据。
Image Vis Comput. 2014 Oct;32(10):641-647. doi: 10.1016/j.imavis.2013.12.007.
4
Vagus Nerve Stimulation (VNS) and Treatment of Depression: To the Brainstem and Beyond.迷走神经刺激(VNS)与抑郁症治疗:通向脑干及其他部位
Psychiatry (Edgmont). 2006 May;3(5):54-63.
5
Detection of clinical depression in adolescents' speech during family interactions.青少年在家庭互动中的言语中临床抑郁的检测。
IEEE Trans Biomed Eng. 2011 Mar;58(3):574-86. doi: 10.1109/TBME.2010.2091640. Epub 2010 Nov 11.
6
Linking "big" personality traits to anxiety, depressive, and substance use disorders: a meta-analysis.将“大”人格特质与焦虑、抑郁和物质使用障碍联系起来:一项荟萃分析。
Psychol Bull. 2010 Sep;136(5):768-821. doi: 10.1037/a0020327.
7
Nonverbal communication in psychotherapy.心理治疗中的非言语沟通。
Psychiatry (Edgmont). 2010 Jun;7(6):38-44.
8
Affective cognition and its disruption in mood disorders.情感认知及其在心境障碍中的障碍。
Neuropsychopharmacology. 2011 Jan;36(1):153-82. doi: 10.1038/npp.2010.77. Epub 2010 Jun 23.
9
Antidepressant drug effects and depression severity: a patient-level meta-analysis.抗抑郁药的效果与抑郁严重程度:患者水平的荟萃分析。
JAMA. 2010 Jan 6;303(1):47-53. doi: 10.1001/jama.2009.1943.
10
A survey of affect recognition methods: audio, visual, and spontaneous expressions.情感识别方法综述:音频、视觉与自发表情
IEEE Trans Pattern Anal Mach Intell. 2009 Jan;31(1):39-58. doi: 10.1109/TPAMI.2008.52.

抑郁严重程度评估访谈中的二元行为分析

Dyadic Behavior Analysis in Depression Severity Assessment Interviews.

作者信息

Scherer Stefan, Hammal Zakia, Yang Ying, Morency Louis-Philippe, Cohn Jeffrey F

机构信息

USC Institute for Creative Technologies, 12015 Waterfront Dr. Playa Vista, CA.

Carnegie Mellon University, 5000 Fifth Avenue, Pittsburgh, PA.

出版信息

Proc ACM Int Conf Multimodal Interact. 2014 Nov;2014:112-119. doi: 10.1145/2663204.2663238.

DOI:10.1145/2663204.2663238
PMID:28345076
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5365085/
Abstract

Previous literature suggests that depression impacts vocal timing of both participants and clinical interviewers but is mixed with respect to acoustic features. To investigate further, 57 middle-aged adults (men and women) with Major Depression Disorder and their clinical interviewers (all women) were studied. Participants were interviewed for depression severity on up to four occasions over a 21 week period using the Hamilton Rating Scale for Depression (HRSD), which is a criterion measure for depression severity in clinical trials. Acoustic features were extracted for both participants and interviewers using COVAREP Toolbox. Missing data occurred due to missed appointments, technical problems, or insufficient vocal samples. Data from 36 participants and their interviewers met criteria and were included for analysis to compare between high and low depression severity. Acoustic features for participants varied between men and women as expected, and failed to vary with depression severity for participants. For interviewers, acoustic characteristics strongly varied with severity of the interviewee's depression. Accommodation - the tendency of interactants to adapt their communicative behavior to each other - between interviewers and interviewees was inversely related to depression severity. These findings suggest that interviewers modify their acoustic features in response to depression severity, and depression severity strongly impacts interpersonal accommodation.

摘要

以往的文献表明,抑郁症会影响参与者和临床访谈者的语音时间,但在声学特征方面存在分歧。为了进一步研究,对57名患有重度抑郁症的中年成年人(男性和女性)及其临床访谈者(均为女性)进行了研究。在21周的时间里,使用汉密尔顿抑郁量表(HRSD)对参与者进行了多达四次的抑郁症严重程度访谈,该量表是临床试验中抑郁症严重程度的标准测量方法。使用COVAREP工具包提取了参与者和访谈者的声学特征。由于错过预约、技术问题或语音样本不足而出现了数据缺失。来自36名参与者及其访谈者的数据符合标准,并被纳入分析,以比较高抑郁症严重程度和低抑郁症严重程度之间的差异。参与者的声学特征在男性和女性之间如预期的那样有所不同,但在参与者中并未随抑郁症严重程度而变化。对于访谈者来说,声学特征与受访者的抑郁症严重程度密切相关。访谈者和受访者之间的顺应——互动者相互调整其交际行为的倾向——与抑郁症严重程度呈负相关。这些发现表明,访谈者会根据抑郁症严重程度调整其声学特征,并且抑郁症严重程度会强烈影响人际顺应。