Robust Classification of Parkinson’s Speech: an Approximation to a Scenario With Non-controlled Acoustic Conditions

Several studies have shown Parkinson’s disease (PD) can be detected from speech signals. However, most of them focus on clean speech recorded under controlled noise environments and standardized equipment, which may limit their ease of access and application in realistic scenarios. In this study, we analyze the performance of PD detection models when a modified version of the ORCA-CLEAN denoiser is applied. The denoiser was re-trained on human speech to clean noisy pathological speech signals before the classification stage. The residual signals were explored to determine whether the denoising process effectively removes unwanted noise while preserving essential speech features related to the disease and therefore relevant to PD detection. The experiments were conducted using recordings of the PC-GITA database along with replicas of it created by adding different levels of artificial noise. The results demonstrate remarkable robustness in classification accuracy despite high levels of added noise. These findings suggest that integrating denoising techniques into the PD classification pipeline can lead to reliable and accurate results even under non-ideal environments. These results will potentially lead to more accessible technology with application in real-world scenarios.