The TORGO database of dysarthric articulation consists of aligned acoustics and measured 3D articulatory features from speakers with either cerebral palsy (CP) or amyotrophic lateral sclerosis (ALS), which are two of the most prevalent causes of speech disability (Kent and Rosen, 2004), and matched controls. This database, called TORGO, is the result of a collaboration between Computer Science and Speech-Language Pathology departments at the University of Toronto and the Holland-Bloorview Kids Rehab hospital in Toronto.

This dataset contains 2000 samples for dysarthric males, dysarthric females, non-dysarthric males, and non-dysarthric females.

Originally TORGO database contains 18GB of data, to download and for more information on data, please refer to the following link,
http://www.cs.toronto.edu/~complingweb/data/TORGO/torgo.html

This database should be used only for academic purposes.

Database / Licence Reference:
Rudzicz, F., Namasivayam, A.K., Wolff, T. (2012) The TORGO database of acoustic and articulatory speech from speakers with dysarthria. Language Resources and Evaluation, 46(4), pages 523--541.

Data Information:

It contains four folders with descriptions below,

• dysarthria_female: 500 samples of dysarthric female audio recorded on different sessions.
• dysarthria_male: 500 samples of dysarthric male audio recorded on different sessions.
• non _dysarthria _female: 500 samples of non-dysarthric female audio recorded on different sessions.
• non _dysarthria _male: 500 samples of non-dysarthric male audio recorded on different sessions.

data.csv
filename: audio file path
is_dysarthria: non-dysarthria or dysarthria
gender: male or female

Application of the data,

• Applying deep learning technology to classify dysarthria and non-dysarthria patients

References:
Dumane, P., Hungund, B., Chavan, S. (2021). Dysarthria Detection Using Convolutional Neural Network. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_45