利用扩张型CNNS在自然环境中进行抑郁检测的声道协调

Zhaocheng Huang, J. Epps, Dale Joachim
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引用次数: 26

摘要

从语音中检测抑郁症继续吸引着大量的研究关注,但仍然是一个重大挑战,特别是当语音是在自然环境中从各种智能手机获取时。基于声道协调的分析方法通过多尺度相互关联的特征值来量化特征之间随时间的关系,在抑郁症和认知障碍检测中显示出很大的前景。在这些方法成功的激励下,本文提出了一种利用卷积神经网络(cnn)提取全声道协调(FVTC)特征的新方法,克服了以前的缺点。对拟议的FVTC-CNN结构在抑郁语音数据上的评估显示,相对于现有的VTC基线系统,在干净条件下和嘈杂条件下的平均F1分数至少提高了16.4%。
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Exploiting Vocal Tract Coordination Using Dilated CNNS For Depression Detection In Naturalistic Environments
Depression detection from speech continues to attract significant research attention but remains a major challenge, particularly when the speech is acquired from diverse smartphones in natural environments. Analysis methods based on vocal tract coordination have shown great promise in depression and cognitive impairment detection for quantifying relationships between features over time through eigenvalues of multi-scale cross-correlations. Motivated by the success of these methods, this paper proposes a novel way to extract full vocal tract coordination (FVTC) features by use of convolutional neural networks (CNNs), overcoming earlier shortcomings. Evaluations of the proposed FVTC-CNN structure on depressed speech data show improvements in mean F1 scores of at least 16.4% under clean conditions and comparable results under noisy conditions relative to existing VTC baseline systems.
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