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

arXiv:2609.38203 · cs.CL, cs.AI, eess.AS · Submitted 2026-09-22 · Read on arXiv

cs.CL, cs.AI, eess.AS

Submitted: 2026-09-22

Updated: 2026-09-22

License: http://creativecommons.org/licenses/by/4.0/

The gist: Monitoring cognitive impairment (CI) in motor neuron disease (MND) is essential for timely treatment and care, yet challenging due to co-occurring speech difficulties.

Terminology

Amyotrophic Lateral Sclerosis — 10× in this paper
Alignment — 6× in this paper · explained in 1 episode
Automatic Speech Recognition — 5× in this paper
Transformer — 5× in this paper · explained in 2 episodes
Verbal Fluency Index — 5× in this paper
Accuracy — 4× in this paper · explained in 1 episode
Motor Neuron Disease — 4× in this paper
Embeddings — 3× in this paper · explained in 2 episodes
Automatic Speech Recognition (ASR) — 2× in this paper · explained in 2 episodes
Generative AI — 2× in this paper · explained in 2 episodes
AUC — 1× in this paper · explained in 2 episodes
Fine-Tuning — 1× in this paper · explained in 5 episodes
Foundation Model — 1× in this paper · explained in 3 episodes
Foundation Models — 1× in this paper · explained in 7 episodes
Normalised Root Mean Squared Error — 1× in this paper
Random Forest — 1× in this paper
Representation Learning — 1× in this paper · explained in 2 episodes
Self-Supervised Learning — 1× in this paper · explained in 7 episodes
Support Vector Regression — 1× in this paper
Support Vector Regression (SVR) — 1× in this paper · explained in 2 episodes
Transformer Architecture — 1× in this paper · explained in 2 episodes
Transformer Models — 1× in this paper · explained in 2 episodes
Uncertainty Quantification — 1× in this paper · explained in 8 episodes
alpha — 1× in this paper · explained in 1 episode

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.

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

Terminology

Abstract

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