FBSE-FTFCWT-Based Novel Automated Framework for Dysarthric Speech Detection

Neurological injuries or neurodegenerative diseases can lead to dysarthria, a condition that impairs speech intelligibility. Accurate detection of dysarthria and its severity from speech signals are crucial for advancing smart healthcare solutions. This study presents an automated system for dysarthria detection and severity classification, using a Fourier-Bessel series expansion-based flexible time-frequency coverage wavelet transform (FBSE-FTFCWT) and an autoencoder. Initially, FBSE-FTFCWT decomposes the speech signal into 16 sub-band signals, which are used as an input in the form of tensor for autoencoders to generate latent representation. This latent representation is subsequently used for dysarthric speech and its severity level detection. The proposed framework outperformed the current state-of-the-art in classifying dysarthric and normal speech on the UA-speech dataset, achieving 3.28% higher accuracy. Additionally, for the dysarthric severity detection task using speech signals from the same dataset, it showed 3.1% improvement in accuracy over existing methods.