Preventing Veteran suicide is a national priority. The US Department of Veterans Affairs (VA) collects, analyzes, and publishes data to inform suicide prevention strategies. Current approaches for detecting suicidal ideation mostly rely on patient self report which are inadequate and time consuming. In this research study, our goal was to automate suicidal ideation detection from acoustic and linguistic features of an individual's speech using machine learning (ML) algorithms. Using voice data collected from Veterans enrolled in a large interventional study on Gulf War Illness at the Washington DC VA Medical Center, we conducted an evaluation of the performance of different ML approaches in achieving our objective. By fitting both classical ML and deep learning models to the dataset, we identified the algorithms that were most effective for each feature set. Among classical machine learning algorithms, the Support Vector Machine (SVM) trained on acoustic features performed best in classifying suicidal Veterans. Among deep learning methods, the Convolutional Neural Network (CNN) trained on the linguistic features performed best. Our study shows that speech analysis in a machine learning pipeline is a promising approach for detecting suicidality among Veterans.

中文翻译：

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基于语音的声学和语言特征检测美国退伍军人自杀意念的机器学习方法

预防退伍军人自杀是国家优先事项。美国退伍军人事务部 (VA) 收集、分析和发布数据以告知自杀预防策略。当前检测自杀意念的方法主要依赖于患者的自我报告，这是不充分且耗时的。在这项研究中，我们的目标是使用机器学习 (ML) 算法从个人语音的声学和语言特征中自动检测自杀意念。我们使用从华盛顿特区弗吉尼亚医疗中心参加海湾战争疾病大型干预研究的退伍军人那里收集的语音数据，对不同 ML 方法在实现我们的目标方面的性能进行了评估。通过将经典 ML 和深度学习模型都拟合到数据集，我们确定了对每个特征集最有效的算法。在经典的机器学习算法中，对声学特征进行训练的支持向量机 (SVM) 在对有自杀倾向的退伍军人进行分类方面表现最好。在深度学习方法中，对语言特征进行训练的卷积神经网络 (CNN) 表现最好。我们的研究表明，机器学习管道中的语音分析是检测退伍军人自杀倾向的一种很有前景的方法。