Speech patterns have been identified as potential diagnostic markers for
neuropsychiatric conditions. However, most studies only compare a single
clinical group to healthy controls, whereas clinical practice often requires
differentiating between multiple potential diagnoses (multiclass settings). To
address this, we assembled a dataset of repeated recordings from 420
participants (67 with major depressive disorder, 106 with schizophrenia and 46
with autism, as well as matched controls), and tested the performance of a
range of conventional machine learning models and advanced Transformer models
on both binary and multiclass classification, based on voice and text features.
While binary models performed comparably to previous research (F1 scores
between 0.54-0.75 for autism spectrum disorder, ASD; 0.67-0.92 for major
depressive disorder, MDD; and 0.71-0.83 for schizophrenia); when
differentiating between multiple diagnostic groups performance decreased
markedly (F1 scores between 0.35-0.44 for ASD, 0.57-0.75 for MDD, 0.15-0.66 for
schizophrenia, and 0.38-0.52 macro F1). Combining voice and text-based models
yielded increased performance, suggesting that they capture complementary
diagnostic information.
Our results indicate that models trained on binary classification may learn
to rely on markers of generic differences between clinical and non-clinical
populations, or markers of clinical features that overlap across conditions,
rather than identifying markers specific to individual conditions. We provide
recommendations for future research in the field, suggesting increased focus on
developing larger transdiagnostic datasets that include more fine-grained
clinical features, and that can support the development of models that better
capture the complexity of neuropsychiatric conditions and naturalistic
diagnostic assessment.

本研究比较了基于语音和文本特征的二元分类和多类分类模型在区分多种神经精神障碍时的表现，结果显示联合模型表现更佳，同时提出今后应该关注更具代表性的跨诊断数据集以及精细化的临床特征。