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Post-stroke speech disorders impair communication and rehabilitation outcomes, often requiring prolonged, intensive therapy sessions. The diversity of symptoms, coupled with the high cost and logistical burden of traditional speech therapy, underscores the need for accurate, automatic assessment to support tailored interventions. Leveraging a purpose-built database of stroke patients, this study introduces a feature-driven framework integrating traditional acoustic features with physiologically informed glottal parameters for classifying impaired speech after stroke. Evaluating unimodal, combined, and SHAP-derived feature configurations, our approach achieved a 97% F1-score in distinguishing pathological from healthy speech. These results highlight the potential of combining clinically meaningful glottal and acoustic information to support early speech deterioration detection, enhancing accessibility and personalised rehabilitation strategies for improved patient outcomes.