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

TL;DR

This paper introduces an interpretable framework for Parkinson's diagnosis using self-supervised speech representations, enhancing transparency and clinical trustworthiness while maintaining competitive accuracy across diverse speech benchmarks.

Contribution

The paper presents a novel cross-attention based interpretability framework tailored for self-supervised speech embeddings in Parkinson's diagnosis, addressing the black-box challenge.

Findings

Effective identification of meaningful speech patterns in embeddings.

Enhanced interpretability through temporal and embedding analysis.

Competitive classification accuracy with robustness in cross-lingual scenarios.

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.