Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis

• URL: http://arxiv.org/abs/2412.02006v2
• Date: Mon, 10 Feb 2025 13:25:26 GMT
• Title: Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis
• Authors: David Gimeno-Gómez, Catarina Botelho, Anna Pompili, Alberto Abad, Carlos-D. Martínez-Hinarejos,
• Abstract summary: We propose a novel, interpretable framework specifically designed to support Parkinson's Disease diagnosis. Through the design of simple yet effective cross-attention mechanisms, the proposed framework offers interpretability from two distinct but complementary perspectives. Our method results competitive with state-of-the-art approaches, while also demonstrating robustness in cross-lingual scenarios.
• Score: 9.91077163490596
• License:
• Abstract: Recent works in pathological speech analysis have increasingly relied on powerful self-supervised speech representations, leading to promising results. However, the complex, black-box nature of these embeddings and the limited research on their interpretability significantly restrict their adoption for clinical diagnosis. To address this gap, we propose a novel, interpretable framework specifically designed to support Parkinson's Disease (PD) diagnosis. Through the design of simple yet effective cross-attention mechanisms for both embedding- and temporal-level analysis, the proposed framework offers interpretability from two distinct but complementary perspectives. Experimental findings across five well-established speech benchmarks for PD detection demonstrate the framework's capability to identify meaningful speech patterns within self-supervised representations for a wide range of assessment tasks. Fine-grained temporal analyses further underscore its potential to enhance the interpretability of deep-learning pathological speech models, paving the way for the development of more transparent, trustworthy, and clinically applicable computer-assisted diagnosis systems in this domain. Moreover, in terms of classification accuracy, our method achieves results competitive with state-of-the-art approaches, while also demonstrating robustness in cross-lingual scenarios when applied to spontaneous speech production.

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