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 diseaseâ€, 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 Name | Role | Type | Description | Units | Missing Values |
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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.
Dataset Files
| File | Size |
|---|---|
| LSVT_voice_rehabilitation.xlsx | 593.8 KB |
| LSVT_feature_names.txt | 4.8 KB |
Reviews
There are no reviews for this dataset yet.
pip install ucimlrepo
from ucimlrepo import fetch_ucirepo # fetch dataset lsvt_voice_rehabilitation = fetch_ucirepo(id=282) # data (as pandas dataframes) X = lsvt_voice_rehabilitation.data.features y = lsvt_voice_rehabilitation.data.targets # metadata print(lsvt_voice_rehabilitation.metadata) # variable information print(lsvt_voice_rehabilitation.variables)
Tsanas, A. (2014). LSVT Voice Rehabilitation [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C52S4Z.
Creators
Athanasios Tsanas
DOI
License
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
This allows for the sharing and adaptation of the datasets for any purpose, provided that the appropriate credit is given.