1. Unveiling Interpretability in Self-Supervised Speech Representations for Parkinson's Diagnosis
- Author
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Gimeno-Gómez, David, Botelho, Catarina, Pompili, Anna, Abad, Alberto, and Martínez-Hinarejos, Carlos-D.
- Subjects
Computer Science - Computer Vision and Pattern Recognition - 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., Comment: Submitted to the Special Issue on "Modelling and Processing Language and Speech in Neurodegenerative Disorders" published by Journal of Selected Topics in Signal Processing (JSTSP)
- Published
- 2024