Automatic quality control and enhancement for voice-based remote Parkinson’s disease detection

• Published in: Speech communication Vol. 127; pp. 1 - 16
• Main Authors: Poorjam, Amir Hossein, Kavalekalam, Mathew Shaji, Shi, Liming, Raykov, Jordan P., Jensen, Jesper Rindom, Little, Max A., Christensen, Mads Græsbøll
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.03.2021; Elsevier Science Ltd
• Subjects: Acoustic mismatch; Acoustics; Algorithms; Automatic control; Background noise; Control methods; Degradation; Parkinson's disease; Parkinson’s disease detection; Quality control; Reverberation; Speech enhancement; Voice control; Voice recognition; Speech enhancement; Acoustic mismatch; Parkinson’s disease detection; Quality control
• ISSN: 0167-6393; 1872-7182
• DOI: 10.1016/j.specom.2020.12.007

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. •Acoustic mismatch degrades the performance of voice-based PD detection systems.•Automatic quality control can identify the presence and type of degradations in signals.•Identifying degradations can inform the choice of an optimal enhancement method.•Appropriate enhancement algorithms can effectively compensate the acoustic mismatch.