Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.

负面的精神分裂特征可能可以使用客观的声音分析进行数字表型。先前的尝试在这方面取得了好坏参半的成功，可能是因为声学分析依赖于小的、受限的特征集。我们利用机器学习来（a）使用大型声学特征集优化和交叉验证自我报告的负面分裂型的预测模型，（b）评估模型性能作为性别和说话任务的函数，（c）了解潜在的潜在机制通过评估这些模型中的关键声学特征来识别消极的精神分裂特征，以及（d）检查模型与临床症状和认知功能的融合表现。准确性良好（> 80%），并且通过考虑口语任务和性别而得到提高。然而，被认为最能预测负面精神分裂特征的特征通常不被认为对其概念定义至关重要。讨论了验证和实施数字表型分析以理解和量化负性精神分裂的意义。