Idiopathic Parkinson’s disease is associated with motor speech impairments collectively referred to as hypokinetic dysarthria, which can appear at early disease stages and remain challenging to assess objectively in clinical practice. Most automatic assessment studies rely on individual speech corpora analyzed in isolation, leaving open questions regarding their comparability and their suitability for joint use within unified classification frameworks. This study explicitly investigates the cross-corpus comparability of existing Parkinsonian speech datasets designed for hypokinetic dysarthria assessment. Rather than assuming their compatibility, we evaluate it empirically through the generalization performance of classification systems trained on single or multiple corpora. We examine which datasets can be effectively combined and whether multi-corpus training improves robustness across heterogeneous recording conditions and speech tasks. Four corpora are evaluated under intra-corpus, cross-corpus, and out-of-domain settings. Results demonstrate that multi-corpus training enhances robustness and generalization performance, while also revealing substantial differences in cross-dataset compatibility. These findings provide a clearer understanding of the degree of comparability between existing resources and offer practical guidelines for the design of future corpora and more generalizable tools for the automatic clinical assessment of Parkinsonian speech.

Nous explorons l’évaluation de la tâche de description automatique de scènes audio à travers une approche indirecte basée sur la réponse aux questions sur des documents audio. En l’absence de métriques d’évaluation robustes et automatiques pour la tâche de description automatique de scènes audio, nous nous appuyons sur le benchmark MMAU, un jeu de questions à choix multiple sur des extraits audio variés. Nous introduisons une architecture en cascade qui dépasse les performances de certains modèles de référence de taille comparable. Toutefois, nos résultats mettent en évidence des limitations du benchmark MMAU, notamment un biais textuel et une capacité limitée à évaluer l’intégration conjointe des informations relatives à la parole et aux événements sonores. Nous suggérons des pistes d’amélioration pour rendre les évaluations futures plus fidèles aux enjeux de la tâche de description automatique de scènes audio.