Cross-Lingual Multi-Granularity Framework for Interpretable Parkinson's Disease Diagnosis from Speech

Ilias Tougui, Mehdi Zakroum, Mounir Ghogho
10/4/2025
cs.CLcs.SDeess.AS

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Abstract

Parkinson's Disease (PD) affects over 10 million people worldwide, with speech impairments in up to 89% of patients. Current speech-based detection systems analyze entire utterances, potentially overlooking the diagnostic value of specific phonetic elements. We developed a granularity-aware approach for multilingual PD detection using an automated pipeline that extracts time-aligned phonemes, syllables, and words from recordings. Using Italian, Spanish, and English datasets, we implemented a bidirectional LSTM with multi-head attention to compare diagnostic performance across the different granularity levels. Phoneme-level analysis achieved superior performance with AUROC of 93.78% +- 2.34% and accuracy of 92.17% +- 2.43%. This demonstrates enhanced diagnostic capability for cross-linguistic PD detection. Importantly, attention analysis revealed that the most informative speech features align with those used in established clinical protocols: sustained vowels (/a/, /e/, /o/, /i/) at phoneme level, diadochokinetic syllables (/ta/, /pa/, /la/, /ka/) at syllable level, and /pataka/ sequences at word level. Source code will be available at https://github.com/jetliqs/clearpd.

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Cite this paper

@article{tougui2025crosslingual,
  title  = {Cross-Lingual Multi-Granularity Framework for Interpretable Parkinson's Disease Diagnosis from Speech},
  author = {Ilias Tougui and Mehdi Zakroum and Mounir Ghogho},
  year   = {2025},
  eprint = {2510.03758},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2510.03758v1}
}

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