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通过扬声器脱而出来提高基于语音的抑郁症检测的准确性和隐私性

Vijay Ravi¹, Jinhan Wang¹, Jonathan Flint²

¹Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, 90095, USA.

Computer speech & language

|February 5, 2024

概括

这项研究引入了新的方法来使用语音检测主要抑郁障碍 (MDD),同时通过将发言者的身份与抑郁信号分开来保护患者的隐私. 这种方法提高了抑郁症检测的准确性,并增强了语音隐私.

科学领域:

人工智能的人工智能
计算语言学计算语言学
精神病学是一个精神病学.

背景情况:

语音信号越来越被认为是心理健康评估的有价值生物标志物,特别是用于自动检测重度抑郁症 (MDD).
目前的方法通常依赖于说话者的身份特征,这引发了对患者隐私和可能影响诊断准确性的潜在偏见的担忧.
需要先进的技术,可以从语音中提取与抑郁相关的信息,而不会影响敏感的说话者特定数据.

研究的目的:

开发和评估新的方法来解开语音标识信息与语音信号中的抑郁相关信息的纠.
提高自动重大抑郁症 (MDD) 检测的准确性,同时提高语音隐私.
克服现有方法的局限性,这些方法过度依赖说话者的身份特征.

主要方法:

提出了四种不同的解方法:对抗性扬声器识别 (SID) -损失最大化 (ADV),SID损失与差异等同 (LEV),使用交叉 (LECE) 的SID损失等同,以及使用KL分歧 (LEKLD) 的SID损失等同.
在DAIC-WOZ (英语) 和EATD (汉语) 数据集上使用各种输入功能 (例如ComparE16,原始音频) 和模型架构进行实验.
在MDD检测性能 (F1-Scores) 和语音隐私属性中使用语音独特性 () 和脱身份分数 (DeID) 的量化改进.

主要成果:

采用ComparE16功能的LECE方法在DAIC-WOZ数据集上实现了MDD检测的80%的最先进的 (SOTA) 音频F1-Score,其值为-1.1dB,DeID为85%.
使用原始音频信号的ADV方法在EATD数据集上获得了72.38%的F1-Score,超过了多模式SOTA,以及0.89 dB的DeID和51.21%的DeID.
与基线方法相比,所有拟议的解方法都在抑郁症检测准确度和语音隐私指标方面都取得了改进.

结论:

将发言人身份信息与与抑郁相关的语音线索脱而出,是保护隐私的心理健康评估的可行策略.
提出的方法为开发基于语音的强大和道德的抑郁症检测系统提供了有希望的方向.
减少对扬声器身份特征的依赖,提高了系统性能,并保护了敏感的患者信息.

关键词:

抑郁症检测器可以检测抑郁症.隐私隐私隐私隐私隐私隐私发言人-解开纠的问题

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