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首页> 外文会议>International Conference on Affective Computing and Intelligent Interaction >Detection of depression in speech
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Detection of depression in speech

机译：检测语音抑郁症

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Depression is a common mental disorder and one of the main causes of disability worldwide. Lacking objective depressive disorder assessment methods is the key reason that many depressive patients can't be treated properly. Developments in affective sensing technology with a focus on acoustic features will potentially bring a change due to depressed patient's slow, hesitating, monotonous voice as remarkable characteristics. Our motivation is to find out a speech feature set to detect, evaluate and even predict depression. For these goals, we investigate a large sample of 300 subjects (100 depressed patients, 100 healthy controls and 100 high-risk people) through comparative analysis and follow-up study. For examining the correlation between depression and speech, we extract features as many as possible according to previous research to create a large voice feature set. Then we employ some feature selection methods to eliminate irrelevant, redundant and noisy features to form a compact subset. To measure effectiveness of this new subset, we test it on our dataset with 300 subjects using several common classifiers and 10-fold cross-validation. Since we are collecting data currently, we have no result to report yet.
机译：抑郁症是一种常见的精神障碍和全球残疾的主要原因之一。缺乏客观抑郁症评估方法是许多抑郁症患者无法妥善治疗的关键原因。情感传感技术的开发，重点放在声学特征上会导致由于抑郁患者的慢，犹豫不决，犹豫不决的声音引起了变化。我们的动机是找出一个语音功能，以检测，评估甚至预测抑郁症。对于这些目标，我们通过比较分析和随访研究调查300名受试者（100名抑郁症患者，100名健康对照和100名高危人员）的大型样本。为了检查抑郁和语音之间的相关性，我们根据以前的研究提取尽可能多的功能，以创建一个大型语音功能集。然后我们使用一些特征选择方法来消除无关，冗余和嘈杂的功能以形成紧凑的子集。为了测量这个新子集的有效性，我们将其在我们的数据集上与300个主题的数据集测试，使用几个常见的分类器和10倍交叉验证。由于我们目前正在收集数据，因此我们没有结果才能报告。

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