Automatic Detection of Depression in Speech Using Gaussian Mixture Modeling with Factor Analysis
Automatic Detection of Depression in Speech Using Gaussian Mixture Modeling with Factor Analysis
复制标题
使用高斯混合模型和因子分析自动检测语音抑郁症
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
A. McCree
中科院分区：
文献类型：
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作者：
D. Sturim;P. Torres;T. Quatieri;Nicolas Malyska;A. McCree
1 对于平民和军人来说，在早期阶段识别重度抑郁症并在严重症状出现之前进行干预变得越来越重要。为了更有效地监测抑郁严重程度的目标，我们研究了抑郁状态的自动分类器，该分类器具有减轻由于数据可变性（例如与抑郁程度无关的扬声器和通道效应）造成的麻烦的重要特性。为了评估我们的措施，我们使用了一个包含 35 位说话者的自由回答语音数据库，其中包含为期六周的抑郁症治疗受试者，以及标准临床 HAMD 抑郁评级。初步实验表明，通过减轻麻烦，从而将抑郁症的严重程度作为一个类别来关注，我们可以显着提高基于基线高斯混合模型的分类器的分类准确性。
1 Of increasing importance in the civilian and military population is the recognition of Major Depressive Disorder at its earliest stages and intervention before the onset of severe symptoms. Toward the goal of more effective monitoring of depression severity, we investigate automatic classifiers of depression state, that have the important property of mitigating nuisances due to data variability, such as speaker and channel effects, unrelated to levels of depression. To assess our measures, we use a 35-speaker free-response speech database of subjects treated for depression over a six-week duration, along with standard clinical HAMD depression ratings. Preliminary experiments indicate that by mitigating nuisances, thus focusing on depression severity as a class, we can significantly improve classification accuracy over baseline Gaussian-mixture-model-based classifiers.