LSVT Voice Rehabilitation

Donated on 2/18/2014

126 samples from 14 participants, 309 features. Aim: assess whether voice rehabilitation treatment lead to phonations considered 'acceptable' or 'unacceptable' (binary class classification problem).

Dataset Characteristics

Multivariate

Subject Area

Health and Medicine

Associated Tasks

Classification

Feature Type

Real

# Instances

126

# Features

-

Dataset Information

Additional Information

The original paper demonstrated that it is possible to correctly replicate the experts' binary assessment with approximately 90% accuracy using both 10-fold cross-validation and leave-one-subject-out validation. We experimented with both random forests and support vector machines, using standard approaches for optimizing the SVM's hyperparameters. It will be interesting if researchers can improve on this finding using advanced machine learning tools. Details for the dataset can be found on the following paper. A. Tsanas, M.A. Little, C. Fox, L.O. Ramig: “Objective automatic assessment of rehabilitative speech treatment in Parkinson’s disease”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, pp. 181-190, January 2014 A freely available preprint is availabe from the first author's website.

Has Missing Values?

No

Variables Table

Variable NameRoleTypeDemographicDescriptionUnitsMissing Values
no
no
no
no
no
no
no
no
no
no

0 to 10 of 309

Additional Variable Information

Each attribute (feature) corresponds to the application of a speech signal processing algorithm which aims to characterise objectively the signal. These algorithms include standard perturbation analysis methods, wavelet-based features, fundamental frequency-based features, and tools used to mine nonlinear time-series. Because of the extensive number of attributes we refer the interested readers to the relevant papers for further details.

Reviews

There are no reviews for this dataset yet.

Login to Write a Review
Download
0 citations
2470 views

Creators

Athanasios Tsanas

License

By using the UCI Machine Learning Repository, you acknowledge and accept the cookies and privacy practices used by the UCI Machine Learning Repository.

Read Policy