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Automatic estimation of verbal fluency index in people with Motor Neuron Disease using ASR alignment and pause modelling

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Computer Science > Computation and Language

arXiv:2609.38203 (cs)
[Submitted on 22 Sep 2026]

Title:Automatic estimation of verbal fluency index in people with Motor Neuron Disease using ASR alignment and pause modelling

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Abstract:Monitoring cognitive impairment (CI) in motor neuron disease (MND) is essential for timely treatment and care, yet challenging due to co-occurring speech difficulties. The Edinburgh Cognitive and Behavioural ALS Screen (ECAS) provides a robust metric for CI assessment, with the Verbal Fluency Index (VFI) a central element. Building on recent advances in automated speech analysis, this study proposes a system for estimating VFI. It leverages a unique MND dataset and combines ASR (WhisperX) and VAD (Silero) with refined timestamping to predict the VFI and extract several clinically interpretable measures. Our approach outperformed systems based on traditional acoustic features and self-supervised embeddings, evaluated using multiple regression algorithms. Clinically inspired features consistently outperformed the other sets, with the best models achieving strong results (P-words: R2 0.9, NRMSE 0.05; S-words: R2 0.8, NRMSE 0.08), demonstrating the feasibility of automated VFI estimation.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Audio and Speech Processing (eess.AS)
Cite as: arXiv:2609.38203 [cs.CL]
  (or arXiv:2609.38203v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2609.38203
arXiv-issued DOI via DataCite

Submission history

From: Bahman Mirheidari [view email]
[v1] Tue, 22 Sep 2026 08:53:02 UTC (1,174 KB)
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