Automatic Quality Control and Enhancement for Voice-Based Remote Parkinson’s Disease Detection

The performance of voice-based Parkinson’s disease
(PD) detection systems degrades when there is an acoustic mismatch
between training and operating conditions caused mainly
by degradation in test signals. In this paper, we address this
mismatch by considering three types of degradation commonly
encountered in remote voice analysis, namely background noise,
reverberation and nonlinear distortion, and investigate how
these degradations influence the performance of a PD detection
system. Given that the specific degradation is known, we explore
the effectiveness of a variety of enhancement algorithms in
compensating this mismatch and improving the PD detection
accuracy. Then, we propose two approaches to automatically
control the quality of recordings by identifying the presence
and type of short-term and long-term degradations and protocol
violations in voice signals. Finally, we experiment with using
the proposed quality control methods to inform the choice of
enhancement algorithm. Experimental results using the voice
recordings of the mPower mobile PD data set under different
degradation conditions show the effectiveness of the quality control
approaches in selecting an appropriate enhancement method
and, consequently, in improving the PD detection accuracy. This
study is a step towards the development of a remote PD detection
system capable of operating in unseen acoustic environments.