Cross-Database Models for the Classification of Dysarthria Presence

Dysarthria is a motor speech disorder that impacts verbal articulation and co-ordination, resulting in slow, slurred and imprecise speech. Automated classification of dysarthria subtypes and severities could provide a useful clinical tool in assessing the onset and progress in treatment. This study represents a pilot project to train models to detect the presence of dysarthria in continuous speech. Subsets of the Universal Access Research Dataset (UA-Speech) and the Atlanta Motor Speech Disorders Corpus (AMSDC) database were utilized in a cross-database training strategy (training on UA-Speech / testing on AMSDC) to distinguish speech with and without dysarthria. In addition to traditional spectral and prosodic features, the current study also includes features based on the Teager Energy Operator (TEO) and the glottal waveform. Baseline results on the UA-Speech dataset maximize wordand participant-level accuracies at 75.3% and 92.9% using prosodic features. However, the cross-training of UA-Speech tested on the AMSDC maximize wordand participant-level accuracies at 71.3% and 90% based on a TEO feature. The results of this pilot study reinforce consideration of dysarthria subtypes in cross-dataset training as well as highlight additional features that may be sensitive to the presence of dysarthria in continuous speech.