L-Flow: Longitudinal Flow Matching for Progression-Aware Speech Biomarker Modeling

Speech is an increasingly promising biomarker for neurodegenerative disorders due to its non-invasive nature, low cost, and suitability for frequent, longitudinal monitoring. However, existing clinical speech datasets are typically small, sparse, and irregularly sampled, limiting the ability to model continuous disease progression and develop robust biomarkers. We propose L-Flow, a progression-aware conditional speech generation framework for longitudinal speech trajectory completion. Given a patient’s speech recordings, timestamps, and clinical progression labels, L-Flow learns a query-specific progression representation to synthesize speech at intermediate time points within the observed recording span. Experiments on two real-world longitudinal speech datasets demonstrate that L-Flow generates synthetic speech with stronger progression consistency than eight baseline augmentation methods. In addition, incorporating L-Flow–augmented data improves downstream regression performance to predict clinical severity, highlighting its utility for speech-based biomarker development.