ADAPTIVE: A Novel Dataset For Acoustic DysArthria deTection through temPoral Inference and Voice Engineering

Dysarthria is a prevalent speech disorder affecting approximately 53% of individuals with speech-related challenges, often arising from neurological conditions such as strokes, cerebral palsy, or Parkinson’s disease. This disorder disrupts the coordination and strength of the muscles used for speech, complicating clear communication, especially with unfamiliar listeners. The impact of dysarthria extends beyond mere communication difficulties; it significantly affects social interactions, job prospects, and educational experiences. Consequently, these challenges can diminish the overall quality of life for those affected, making it imperative to address the disorder effectively.

The research questions guiding this study on pre-screening for dysarthria using machine learning techniques are as follows:
RQ1
What specific acoustic features contribute most to detecting speech dysarthria?
RQ2
How can machine learning algorithms be optimized to enhance the accuracy of dysarthria detection compared to traditional assessment methods?
RQ3
Can a minimum number of MFCC Features along with voice engineered features perform equally or better than taking into account a vast number of MFCC Features only?

Hence we introduce ADAPTIVE: A Novel Dataset For Acoustic DysArthria deTection through temPoral Inference and Voice Engineering.

Corresponding paper in the pipeline is yet to be published.

Please cite this dataset of it helps in your studies or if you build your own dataset using the acoustic-temporal feature engineering scripts, ML models or use findings from our research in your papers.