Investigating the Performance of MIKNN for Objective Speech Intelligibility Assessment of Dysarthric Speech
K. Kowkuntla (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Jorge Martinez – Mentor (TU Delft - Multimedia Computing)
Dimme de Groot – Mentor (TU Delft - Multimedia Computing)
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Abstract
Assessing speech intelligibility for individuals with dysarthria is critical for understanding the severity of motor speech disorders and evaluating speech therapy interventions. Traditional subjective as- sessments, while effective, are resource-intensive and prone to bias, which highlights the need for reliable objective measures. This study investi- gates the applicability of MIKNN (Mutual Infor- mation with K-Nearest Neighbors) as an objective speech intelligibility measure for dysarthric speech, by comparing objective intelligibility scores with subjective ratings. Unlike its proven effective- ness with neurotypical speech, the performance of objective measures on atypical speech, such as dysarthria, remains under-explored. The study compares MIKNN with state-of-the-art measures, including P-STOI and P-ESTOI, using the UA- Speech dataset. Key challenges addressed in- clude adapting MIKNN to handle the temporal and spectral variability inherent in dysarthric speech. The results demonstrate that while MIKNN offers promising correlations with subjective scores, it is outperformed by P-STOI and P-ESTOI.