BDHPD: Bilingual Dual-Head Architecture for Parkinson's Disease Detection from Speech

Model Description

• Self-supervised learning (SSL) with models like microsoft/wavlm-base
• Wavelet-based spectrogram features
• Adaptive Instance Normalization (AdaIN) for domain adaptation
• Convolutional Bottleneck Layers for feature recalibration
• Dual-head classification architecture to handle different speech types (e.g., diadochokinetic and continuous)
• Contrastive learning for embedding space refinement
• Attention pooling for better sequence summarization

Intended Use

• Research in pathological speech detection
• Benchmarking bilingual speech-based PD detection models
• Development of real-world diagnostic support tools in healthcare

Training

• AdamW optimizer
• Linear learning rate scheduling with warmup
• Binary cross-entropy loss for classification
• Contrastive loss via pytorch-metric-learning
• 20 epochs with early stopping
• Balanced batch sampling for both datasets

How to Use

Datasets

• EWA-DB: Slovak pathological and healthy speech
• PC-GITA: Spanish pathological speech

Limitations

• The model is only trained on Slovak and Spanish speakers; cross-lingual generalization outside these languages is untested.
• Sensitive to audio quality-ensure audio is preprocessed with proper VAD and dereverberation.
• Should not be used as a standalone diagnostic tool.

Citation