Seamless Dysfluent Speech Text Alignment for Disordered Speech Analysis
Journal:
arXiv
Published Date:
Jun 5, 2025
Abstract
Accurate alignment of dysfluent speech with intended text is crucial for
automating the diagnosis of neurodegenerative speech disorders. Traditional
methods often fail to model phoneme similarities effectively, limiting their
performance. In this work, we propose Neural LCS, a novel approach for
dysfluent text-text and speech-text alignment. Neural LCS addresses key
challenges, including partial alignment and context-aware similarity mapping,
by leveraging robust phoneme-level modeling. We evaluate our method on a
large-scale simulated dataset, generated using advanced data simulation
techniques, and real PPA data. Neural LCS significantly outperforms
state-of-the-art models in both alignment accuracy and dysfluent speech
segmentation. Our results demonstrate the potential of Neural LCS to enhance
automated systems for diagnosing and analyzing speech disorders, offering a
more accurate and linguistically grounded solution for dysfluent speech
alignment.
