This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-head deep neural architecture for type-based binary classification. One head is specialized for diadochokinetic patterns. The other head looks for natural speech patterns present in continuous spoken utterances. Only one of the two heads is operative accordingly to the nature of the input. Speech representations are extracted from self-supervised learning (SSL) models and wavelet transforms. Adaptive layers, convolutional bottlenecks, and contrastive learning are exploited to reduce variations across languages. Our solution is assessed against two distinct datasets, EWA-DB, and PC-GITA, which cover Slovak and Spanish languages, respectively. Results indicate that conventional models trained on a single language dataset struggle with cross-linguistic generalization, and naive combinations of datasets are suboptimal. In contrast, our model improves generalization on both languages, simultaneously.

本研究解决了在双语环境下通过语音信号检测帕金森病的难题。我们提出了一种特定的双头深度神经网络架构，分别针对快速重复运动模式和自然语音模式进行分类。实验结果表明，该模型在斯洛伐克语和西班牙语的检测中相较于传统单语言模型显著提升了跨语言泛化能力。

双语双头深度模型用于通过语音检测帕金森病