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

This model implements BDHPD, a deep neural network designed to detect Parkinson's Disease (PD) from speech signals, with bilingual support for Slovak and Spanish datasets.

Model Description

BDHPD combines several modern audio processing techniques:

  • 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

The architecture supports bilingual inputs and has been evaluated on EWA-DB (Slovak) and PC-GITA (Spanish).

Intended Use

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

Training

Training was performed using:

  • 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

You can find all information on the GitHub repository: BDHPD GitHub

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

If you use this model or find useful this research work, please cite the following paper:

@inproceedings{laquatra2025bilingual,
  title={Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech},
  author={La Quatra, Moreno and Orozco-Arroyave, Juan Rafael and Siniscalchi, Marco Sabato},
  booktitle={ICASSP 2025 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2025}
}
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Evaluation results