Classification of Dysarthria based on the Levels of Severity. A
  Systematic Review
Afnan Al-Ali Somaya Al-Maadeed Moutaz Saleh Rani Chinnappa Naidu Zachariah C Alex Prakash Ramachandran Rajeev Khoodeeram Rajesh Kumar M
Dysarthria is a neurological speech disorder that can significantly impact
affected individuals' communication abilities and overall quality of life. The
accurate and objective classification of dysarthria and the determination of
its severity are crucial for effective therapeutic intervention. While
traditional assessments by speech-language pathologists (SLPs) are common, they
are often subjective, time-consuming, and can vary between practitioners.
Emerging machine learning-based models have shown the potential to provide a
more objective dysarthria assessment, enhancing diagnostic accuracy and
reliability. This systematic review aims to comprehensively analyze current
methodologies for classifying dysarthria based on severity levels.
Specifically, this review will focus on determining the most effective set and
type of features that can be used for automatic patient classification and
evaluating the best AI techniques for this purpose. We will systematically
review the literature on the automatic classification of dysarthria severity
levels. Sources of information will include electronic databases and grey
literature. Selection criteria will be established based on relevance to the
research questions. Data extraction will include methodologies used, the type
of features extracted for classification, and AI techniques employed. The
findings of this systematic review will contribute to the current understanding
of dysarthria classification, inform future research, and support the
development of improved diagnostic tools. The implications of these findings
could be significant in advancing patient care and improving therapeutic
outcomes for individuals affected by dysarthria.