利用移动电话调查基于语音的自杀意念检测的通用性

IF 3.6 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Pub Date : 2024-01-12 DOI:10.1145/3631452
Arvind Pillai, Trevor Cohen, Dror Ben-Zeev, Subigya Nepal, Weichen Wang, M. Nemesure, Michael Heinz, George Price, D. Lekkas, Amanda C. Collins, Tess Z Griffin, Benjamin Buck, S. Preum, Dror Nicholas Jacobson
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引用次数: 0

摘要

基于手机的语音日记可以捕捉副语言模式,帮助检测自杀意念等精神疾病症状。然而,以往的研究主要是在单一数据集上对机器学习模型进行评估,因此无法了解其在分布变化情况下的性能。在本文中,我们通过跨数据集实验研究了使用手机进行基于语音的自杀意念检测的普适性,实验中使用了四个数据集,包括重度抑郁障碍、听觉言语幻觉、受迫害意念和有自杀意念的学生,样本数为 786 人。我们的结果表明,机器学习和深度学习方法在许多情况下的泛化效果不佳。因此,我们对无监督领域适应(UDA)和半监督领域适应(SSDA)进行了评估,以缓解因分布变化而导致的性能下降。虽然 SSDA 方法表现出了卓越的性能,但它们往往效果不佳,因为它们需要具有有限标签的大型目标数据集来进行对抗性和对比性训练。因此,我们提出了正弦波相似性子采样(S3)方法,该方法通过使用正弦波计算成对分数,为目标域选择最佳源子集。与之前的方法相比,S3 不使用标注的目标数据或转换特征。使用 S3 进行微调可提高深度模型在不同数据集之间的跨数据集性能,从而对泛在技术、心理健康和机器学习产生影响。
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Investigating Generalizability of Speech-based Suicidal Ideation Detection Using Mobile Phones
Speech-based diaries from mobile phones can capture paralinguistic patterns that help detect mental illness symptoms such as suicidal ideation. However, previous studies have primarily evaluated machine learning models on a single dataset, making their performance unknown under distribution shifts. In this paper, we investigate the generalizability of speech-based suicidal ideation detection using mobile phones through cross-dataset experiments using four datasets with N=786 individuals experiencing major depressive disorder, auditory verbal hallucinations, persecutory thoughts, and students with suicidal thoughts. Our results show that machine and deep learning methods generalize poorly in many cases. Thus, we evaluate unsupervised domain adaptation (UDA) and semi-supervised domain adaptation (SSDA) to mitigate performance decreases owing to distribution shifts. While SSDA approaches showed superior performance, they are often ineffective, requiring large target datasets with limited labels for adversarial and contrastive training. Therefore, we propose sinusoidal similarity sub-sampling (S3), a method that selects optimal source subsets for the target domain by computing pair-wise scores using sinusoids. Compared to prior approaches, S3 does not use labeled target data or transform features. Fine-tuning using S3 improves the cross-dataset performance of deep models across the datasets, thus having implications in ubiquitous technology, mental health, and machine learning.
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来源期刊
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies Computer Science-Computer Networks and Communications
CiteScore
9.10
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0.00%
发文量
154
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