Center for Machine Learning and Intelligent Systems
About  Citation Policy  Donate a Data Set  Contact


Repository Web            Google
View ALL Data Sets

LSVT Voice Rehabilitation Data Set
Download: Data Folder, Data Set Description

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

Data Set Characteristics:  

Multivariate

Number of Instances:

126

Area:

Life

Attribute Characteristics:

Real

Number of Attributes:

309

Date Donated

2014-02-19

Associated Tasks:

Classification

Missing Values?

N/A

Number of Web Hits:

20371


Source:

The dataset was created by Athanasios Tsanas (tsanasthanasis '@' gmail.com) of the University of Oxford.


Data Set 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.


Attribute 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.


Relevant Papers:

The dataset was introduced in:
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

Further details about the speech signal processing algorithms can be found in:

A. Tsanas, Accurate telemonitoring of Parkinson’s disease symptom severity using nonlinear speech signal processing and statistical machine learning, D.Phil. (Ph.D.) thesis, University of Oxford, UK, 2012

A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson’s disease symptom severity”, Journal of the Royal Society Interface, Vol. 8, pp. 842-855, 2011

A. Tsanas, M.A. Little, P.E. McSharry, L.O. Ramig: “New nonlinear markers and insights into speech signal degradation for effective tracking of Parkinson’s disease symptom severity”, International Symposium on Nonlinear Theory and its Applications (NOLTA), pp. 457-460, Krakow, Poland, 5-8 September 2010

Preprints are available on the first author's website.



Citation Request:

If you use this dataset, please cite 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


Supported By:

 In Collaboration With:

About  ||  Citation Policy  ||  Donation Policy  ||  Contact  ||  CML