CNN-Based Identification of Parkinson’s Disease from Continuous Speech in Noisy Environments

Abstract

Parkinson’s disease is a progressive neurodegenerative disorder caused by dopaminergic neuron degeneration. Parkinsonian speech impairment is one of the earliest presentations of the disease and, along with tremor, is suitable for pre-diagnosis. It is defined by hypokinetic dysarthria and accounts for respiratory, phonatory, articulatory, and prosodic manifestations. The topic of this article targets artificial-intelligence-based identification of Parkinson’s disease from continuous speech recorded in a noisy environment. The novelty of this work is twofold. First, the proposed assessment workflow performed speech analysis on samples of continuous speech. Second, we analyzed and quantified Wiener filter applicability for speech denoising in the context of Parkinsonian speech identification. We argue that the Parkinsonian features of loudness, intonation, phonation, prosody, and articulation are contained in the speech, speech energy, and Mel spectrograms. Thus, the proposed workflow follows a feature-based speech assessment to determine the feature variation ranges, followed by speech classification using convolutional neural networks. We report the best classification accuracies of 96% on speech energy, 93% on speech, and 92% on Mel spectrograms. We conclude that the Wiener filter improves both feature-based analysis and convolutional-neural-network-based classification performances.

Hybrid Machine Learning Framework for Multistage Parkinson’s Disease Classification Using Acoustic Features of Sustained Korean Vowels

“…Additionally, the remote monitoring of PD using smart devices is gaining popularity, and numerous recent studies have explored the use of mobile devices for voice recording in PD research. For instance, Omberg et al [ 30 ] used an iPhone for their voice recordings, while Farago et al [ 31 ] employed Android smartphone devices (model not specified). On the other hand, Asci et al [ 32 ] utilized various smartphones available on the market (Apple ® , Samsung ® , Huawei ® , Xiaomi ® , and Asus ® ).…”

Leveraging Deep Learning for Fine-Grained Categorization of Parkinson’s Disease Progression Levels through Analysis of Vocal Acoustic Patterns

“…Another study employed a MobileNet CNN model with various types of spectrograms as input. The findings indicated that speech energy spectrograms and mel spectrograms yielded the highest accuracy rates of 96% and 92%, respectively [ 31 ]. A study by Khojasteh et al evaluated the performance of a CNN model on sustained vowel phonation recordings of the /a/ lasting over 5 s. When tested on 2 s voice samples segmented into 815 ms frames, the CNNs achieved a classification accuracy of 75.7%.…”

Harnessing Voice Analysis and Machine Learning for Early Diagnosis of Parkinson's Disease: A Comprehensive Study Across Diverse Datasets

“…Speech-Language Pathologists (SLPs) have historically employed voice analysis to diagnose hypokinetic dysarthria, a speech disorder symptomatic of PD. Nonetheless, navigating the subtleties of these changes requires advanced analytical capabilities beyond conventional statistical approaches (4)(5)(6).…”