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<p>We introduce ParkMAE, a robust multilingual speech foundation model and comprehensive benchmarking system for Parkinson’s disease assessment. We curated multiple large-scale speech datasets comprising approximately 750 h of pretraining data and 100 h of evaluation data across four languages and diverse clinical populations. Our self-supervised masked autoencoder approach, pretrained on this multilingual corpus, demonstrates superior performance achieving 39% F1 score for cross-linguistic diagnosis, outperforming existing acoustic markers (eGeMAPS) by a significant margin and maintaining comparable performance to generic speech models (Whisper), while using 89% fewer parameters. Besides, ParkMAE shows exceptional generalizability to unseen languages without language-specific finetuning. Beyond diagnosis, we systematically evaluate medication state monitoring and disease staging tasks, revealing that despite promising literature reports, current publicly available datasets and speech-based approaches fail to reliably capture these clinical dimensions. For cognitive assessment (MoCA), our model demonstrated predictive capability (F1 = 0.56), suggesting potential for speech-based cognitive monitoring. This comprehensive evaluation establishes both the capabilities and current limitations of speech-based Parkinson’s disease assessment, providing a reproducible framework for future clinical development.</p>

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ParkMAE: a cross-linguistic masked autoencoder framework for robust Parkinson’s disease detection from speech

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月10日

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  • Angelika Ando,
  • Adrien Lesage,
  • Marc De Gennes,
  • Rachid Riad

摘要

We introduce ParkMAE, a robust multilingual speech foundation model and comprehensive benchmarking system for Parkinson’s disease assessment. We curated multiple large-scale speech datasets comprising approximately 750 h of pretraining data and 100 h of evaluation data across four languages and diverse clinical populations. Our self-supervised masked autoencoder approach, pretrained on this multilingual corpus, demonstrates superior performance achieving 39% F1 score for cross-linguistic diagnosis, outperforming existing acoustic markers (eGeMAPS) by a significant margin and maintaining comparable performance to generic speech models (Whisper), while using 89% fewer parameters. Besides, ParkMAE shows exceptional generalizability to unseen languages without language-specific finetuning. Beyond diagnosis, we systematically evaluate medication state monitoring and disease staging tasks, revealing that despite promising literature reports, current publicly available datasets and speech-based approaches fail to reliably capture these clinical dimensions. For cognitive assessment (MoCA), our model demonstrated predictive capability (F1 = 0.56), suggesting potential for speech-based cognitive monitoring. This comprehensive evaluation establishes both the capabilities and current limitations of speech-based Parkinson’s disease assessment, providing a reproducible framework for future clinical development.