Su Zhengyuan, Jiang Huadong, Yang Ying, Hou Xiangqing, Su Yanli, Yang Li
Laboratory of Suicidal Behavior Research, Tianjin University, Tianjin, China.
Institute of Applied Psychology, Tianjin University, Tianjin, China.
J Med Internet Res. 2025 Apr 14;27:e67772. doi: 10.2196/67772.
Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is constrained by factors such as lack of nonverbal communication cues, anonymity, time limits, and single-occasion intervention. Therefore, it is necessary to develop approaches, including acoustic features, for identifying the suicide risk among hotline callers early and quickly. Given the complicated features of sound, adopting artificial intelligence models to analyze callers' acoustic features is promising.
In this study, we investigated the feasibility of using acoustic features to predict suicide risk in crisis hotline callers. We also adopted a machine learning approach to analyze the complex acoustic features of hotline callers, with the aim of developing suicide risk prediction models.
We collected 525 suicide-related calls from the records of a psychological assistance hotline in a province in northwest China. Callers were categorized as low or high risk based on suicidal ideation, suicidal plans, and history of suicide attempts, with risk assessments verified by a team of 18 clinical psychology raters. A total of 164 clearly categorized risk recordings were analyzed, including 102 low-risk and 62 high-risk calls. We extracted 273 audio segments, each exceeding 2 seconds in duration, which were labeled by raters as containing suicide-related expressions for subsequent model training and evaluation. Basic acoustic features (eg, Mel Frequency Cepstral Coefficients, formant frequencies, jitter, shimmer) and high-level statistical function (HSF) features (using OpenSMILE [Open-Source Speech and Music Interpretation by Large-Space Extraction] with the ComParE 2016 configuration) were extracted. Four supervised machine learning algorithms (logistic regression, support vector machine, random forest, and extreme gradient boosting) were trained and evaluated using grouped 5-fold cross-validation and a test set, with performance metrics, including accuracy, F-score, recall, and false negative rate.
The development of machine learning models utilizing HSF acoustic features has been demonstrated to enhance recognition performance compared to models based solely on basic acoustic features. The random forest classifier, developed with HSFs, achieved the best performance in detecting the suicide risk among the models evaluated (accuracy=0.75, F-score=0.70, recall=0.76, false negative rate=0.24).
The results of our study demonstrate the potential of developing artificial intelligence-based early warning systems using acoustic features for identifying the suicide risk among crisis hotline callers. Our work also has implications for employing acoustic features to identify suicide risk in salient voice contexts.
危机热线是早期识别自杀风险的关键途径，这对自杀预防和干预至关重要。然而，在危机热线环境中评估来电者的风险受到多种因素的限制，如缺乏非语言交流线索、匿名性、时间限制和单次干预。因此，有必要开发包括声学特征在内的方法，以便早期快速识别热线来电者中的自杀风险。鉴于声音特征复杂，采用人工智能模型分析来电者的声学特征很有前景。
在本研究中，我们调查了利用声学特征预测危机热线来电者自杀风险的可行性。我们还采用机器学习方法分析热线来电者的复杂声学特征，旨在开发自杀风险预测模型。
我们从中国西北部某省的心理援助热线记录中收集了525个与自杀相关的电话。根据自杀意念、自杀计划和自杀未遂史将来电者分为低风险或高风险，风险评估由18名临床心理学评估人员组成的团队进行核实。共分析了164个明确分类的风险记录，包括102个低风险电话和62个高风险电话。我们提取了273个音频片段，每个片段时长超过2秒，评估人员将其标记为包含与自杀相关的表达，用于后续模型训练和评估。提取了基本声学特征（如梅尔频率倒谱系数、共振峰频率、抖动、闪烁）和高级统计功能（HSF）特征（使用具有ComParE 2016配置的OpenSMILE[通过大空间提取进行开源语音和音乐解释]）。使用分组5折交叉验证和测试集对四种监督机器学习算法（逻辑回归、支持向量机、随机森林和极端梯度提升）进行训练和评估，并使用包括准确率、F分数、召回率和假阴性率在内的性能指标。
与仅基于基本声学特征的模型相比，利用HSF声学特征开发机器学习模型已被证明可提高识别性能。使用HSF开发的随机森林分类器在评估的模型中检测自杀风险方面表现最佳（准确率=0.75，F分数=0.70，召回率=0.76，假阴性率=0.24）。
我们的研究结果表明，利用声学特征开发基于人工智能的早期预警系统以识别危机热线来电者中的自杀风险具有潜力。我们的工作对于在突出语音环境中利用声学特征识别自杀风险也具有启示意义。
