INRIA-Internship for Master2 Students

Title:

Speech analysis for Parkinson's disease detection

Description:

Parkinson's disease (PD) is one of the most common neurodegenerative disorders and its clinical

diagnosis, particularly early one, is still a difficult task. Recent research has shown that the speech

signal may be useful for discriminating people with PD from healthy ones, based on clinical evidence

which suggests that the former typically exhibit some form of vocal disorder. In fact, vocal disorder

may be amongst the earliest PD symptoms, detectable up to five years prior to clinical diagnosis. The

range of symptoms present in speech includes reduced loudness, increased vocal tremor, and

breathiness (noise). Vocal impairment relevant to PD is described as dysphonia (inability to produce

normal vocal sounds) and dysarthria (difficulty in pronouncing words). The use of sustained vowels,

where the speaker is requested to sustain phonation for as long as possible, attempting to maintain

steady frequency and amplitude at a comfortable level, is commonplace in clinical practice. Research

has shown that the sustained vowel “aaaaa” is sufficient for many voice assessment applications,

including PD status prediction.

The first goal of this internship is to implement/improve some state-of-the-art algorithms for dysphonia

measures and use them within an appropriate classifier (like SVM) to discriminate between disordered

and healthy voices. These measures are based on linear and nonlinear speech analysis and are well

documented in [1]. The experiments will be carried on on the well established Kay Elemetrics

Disordered Voice Database (

http://www.kayelemetrics.com/).

The second goal is to try to develop new dysphonia measures based on novel nonlinear speech

analysis algorithms recently developed in the GeoStat team [2]. These algorithms have indeed shown

significant improvements w.r.t. state-of-the-art techniques in many applications including speech

segmentation, glottal inverse filtering and sparse modeling.

The work of this internship will be conducted in collaboration with Dr. Max Little (MediaLab of MIT

and Imperial College of London) and should lead to a PhD fellowship proposition.

References:

[1] A. Tsanas,

M.A. Little, P.E. McSharry, J. Spielman, L.O. Ramig. Novel speech signal processing

algorithms for high-accuracy classification of Parkinson’s disease. IEEE Transactions on Biomedical

Engineering,

59(5):1264-1271. 2012

[2] PhD thesis of Vahid Khanagha. INRIA Bordeaux-Sud Ouest. January 2013.

Prerequisites:

Good level in mathematics and signal/speech processing is necessary, as well as Matlab

and C/C++ programing. Knowledge in machine learning would be an advantage.

Supervisor:

Khalid Daoudi (khalid.daoudi@inria.fr), GeoStat team (http://geostat.bordeaux.inria.fr).

Location:

INRIA- Bordeaux Sud Ouest (http://www.inria.fr/bordeaux). Bordeaux, France.

Starting date:

Fev/Mars 2012.

Duration

: 6 months

Salary:

1200 euros / month