Modelling of Speech Aspects in Parkinson’s Disease by Multitask Deep Learning

Type: MA thesis

Status: finished

Date: March 12, 2019 - May 10, 2019

Supervisors: Elmar Nöth, Juan Camilo Vasquez Correa, Tino Haderlein

Parkinson’s disease is a progressive neurodegenerative disorder with a variety of motor and non-
motor symptoms. Although also other factors are influenced by the disease, the current evaluation
process relies mostly on motor aspects and is often subjective. While speech deficits can be
found in a majority of patients, its analysis is still underrepresented in the clinical assessment. To
increase objectivity and enable long-term monitoring of the patient’s status, several computational
methods have been been proposed in the literature. Along with the success of deep learning,
multitask techniques received more and more attention in recent years. Hence, this Master’s thesis
proposes the use of a multitask neural network-based approach in order to assess multiple aspects
of Parkinsonian speech. The data set included various recordings in numerous sessions obtained
from 94 Parkinson patients and 87 healthy controls. A defined set of statistical features was
extracted for each utterance to be used as input to the model. The multitask setting was defined
with three tasks regarding the distinction between diseased and healthy, as well as, two common
Parkinson rating scales, namely the Movement Disorder Society – Unified Parkinson’s Disease
Rating Scale and the modified Frenchay Dysarthria Assessment. These tasks were supposed to
be optimized together compared to individual networks. In order to get a deeper understanding
with regard to the influence of each task and the specific recording settings, several experiments
with different focuses were conducted. Additionally, the multitask setting was expanded with four
additional tasks to exploit the variability of this method. The experimental results demonstrate the
classification capabilities with accuracy values of 81.73%, 52.45% and 43.56% for the respective
three tasks based on a per session evaluation. These results improve the outcome of individually
trained networks for values between 3 and 16 percent points. Further comparison against an
Adaboost baseline does not show a clear improvement, however, the proposed model delivers
competitive results, especially with focus on other neural network approaches. Thus, this work
gives new insights to the application of multitask deep learning to Parkinsonian speech and builds
the basis for further research in the field