Automatic Detection of Parkinson’s Disease with Connected Speech Acoustic Features: towards a Linguistically Interpretable Approach
Authors: Marta Maffia, Loredana Schettino, Vincenzo Norman Vitale (Università di Napoli L'Orientale, Università di Napoli Federico II, 2023)
Topic: Multimodal

Abstract
Alterations in speech and voice are among the earliest symptoms of Parkinson’s Disease (PD). Nevertheless, the rich information carried by patients’ speech and voice is only partially used for diagnosis and clinical decision-making, which is currently based on holistic ratings of speech intelligibility. An accurate diagnosis could be supported by the application of fully automated analytic methods and machine learning techniques on speech recordings. However, most of the proposed procedures were designed for highly functional but “artificial” vocal paradigms, such as sustained phonation, and consider all the considerable amount of features that can be extracted using automatic systems. In this work, we perform PD detection trials using features extracted from connected speech rather than isolated speech units. Moreover, we support the adopted machine learning-based methods with linguistic considerations to reduce the number of features to some meaningful ones. The main findings highlight that this procedure allows for more accurate, economical, and, most importantly, interpretable discrimination.

Introduction
Parkinson’s Disease (PD) is the most common movement disorder and the second most common neurodegenerative disorder worldwide after Alzheimer disease. It affects more than 2-3% of the population aged 65 and over. Caused by the deterioration or loss of dopaminergic neurons in the substantia nigra of basal ganglia, PD is generally diagnosed based on clinical criteria, using a medical individual’s history and a physical/neurological exam. The loss of dopamine in the central nervous system, along with the anatomical and physiological changes related to the disease, has an impact on laryngeal, respiratory, and articulatory functions of Persons with PD (PwPD). Alterations in speech and voice are in fact among the earliest symptoms of PD, which results in a motor speech disorder called hypokinetic dysarthria. Nevertheless, the rich information carried by patients’ speech and voice is only partially used for diagnosis and clinical decision-making, since the Unified Parkinson’s Disease Rating Scale (UPDRS), a standardized rating tool used to assess the severity and progression of the pathology, only presents one item (item 3.1) that concerns the evaluation of speech. This item is based on the clinician’s perception and mostly considers speech in terms of intelligibility. A deeper understanding of speech and voice phenomena through advanced data analytics methods could therefore be very useful in both the diagnostic phase and in the monitoring of therapy response in PwPD.

Conclusion
The present study provides relevant findings both for the development of PD detection systems and the analysis of Parkinsonian speech characteristics by integrating computational methods with domain-specific linguistic knowledge. The correlation data between the UPDRS ratings concerning PD speakers’ speech ability and the acoustic features automatically extracted from the speech signal corroborate the observation that the specialists’ holistic assessment overlooks, or at least only partially and indirectly considers, acoustic features, which, nonetheless, prove to provide crucial information for the diagnosis. In fact, the speech signal is affected by the condition of the muscles involved in phonation. So, if the vocal apparatus is somewhat compromised as an effect of the muscular impairment due to the disease (dysarthria), the signal should show this. Hence, the relevance of including acoustic features in the assessment of the outbreak and severity of PD. However, fully automated extraction and treatment of speech acoustic features is usually achieved with highly complex systems whose interpretation is quite difficult for both computational scientists, who might be not familiar with PD symptoms and the linguistic value of the features of the speech signal, and for domain experts, who might not be familiar with machine learning methods. Therefore, the design of models in a way that their predictions can be explainable and easily interpretable may actually be most sensible and economical. In fact, this study highlights that not all the possibly considerable acoustic features provide the same amount of information and are actually relevant for discrimination. Moreover, their contribution may vary as a result of the type and span of the linguistic unit used for the feature extraction. More specifically, the classification results show that considering vowel intervals as units of reference for the features extraction is already quite effective. Most effective is, however, considering wider contexts as provided by the inter-pausal phonetic chain intervals, whereas enlarging the vocalic intervals only to the previous consonant (CV intervals as a basic unit) turns out to be noisy rather than informative. Then, on average, the feature subset proved to be most informative, carrying out sufficient information to let the classifiers reach a reasonable detection rate in the considered medical scenario. In particular, the subset mainly includes features concerning spectral distribution, followed by those involving energy and amplitude and finally frequency features (MFCC above all). It is worth noticing that the study has been conducted on continuous speech rather than on isolated phones, syllables or words, to get closer to the normal working dynamic of the vocal apparatus during utterance phonation and avoid artificial effects that may arise when producing single short items. To conclude, supporting automated analytic methods and machine learning techniques with linguistic considerations allows for more accurate, economical and, most importantly, interpretable discrimination. Future work will be devoted to delving deeper into the linguistic analysis of the way the emergent features characterize PD speech and the investigation of the explainability of classification methods based on deep neural networks.