# Parkinson's Disease Classification Framework Using Vocal Dynamics in Connected Speech
## 1 Introduction  
Approximately 7.5 million people all over the world are diagnosed with Parkinson's disease (PD). Since its first description put forth by [PERSON] in 1817, PD has been a neurodegenerative disease that affects motor functioning [1]. With a prevalence of 572 per 100,000 individuals in North America, is the second most common neurodegenerative disorder caused by the degeneration and dysfunction of dopaminergic neurons in the substantia nigra [2]. A 2022 Parkinson's Foundation-backed study revealed that nearly 90,000 people are diagnosed with PD in the U.S. each year [3]. Projections show that the number of people with PD (45 years) will rise to approximately 1,238,000 by 2030 [4]. PD is primarily characterized by motor symptoms like muscle weakness, rigidity, tremor, bradykinesia (slow movement) that includes hypomimia, movement variability, and freezing of gait, and nonmotor symptoms like olfactory dysfunction, anxiety, depression, cognitive deficits, dementia, sleep disorders, and even melanoma [5]. The onset age ranges anywhere between 35 years and 60 years, and over 90% of people with PD are known to develop nonmotor manifestations [6]. However, the disease course and symptom manifestations are known to vary considerably from individual to individual. Such heterogeneity is also observed in response to levodopa medication. Many people with PD experience a drastic decrease in quality of life due to the substantial degeneration of dopaminergic neurons [7, 8]. For over twenty-five years, clinical diagnosis of this highly heterogeneous disease has seen little improvement in terms of accuracy. Before the late 1980s, formal diagnostic criteria for this illness were nonexistent. Today, clinicians rely heavily on the MovementDisorder Society-Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and the Hoehn and Yahr (HY) rating scales to evaluate various aspects and primary motor symptoms in patients' daily lives. However, these subjective ratings can result in incorrect diagnoses due to atypical Parkinsonian conditions, essential tremors, and other dementias [9], which is why research in Parkinson's primarily focuses on disease detection, symptom research, and sustaining people's quality of life. Reports indicate that the onset of prodormal Parkinson's disease is a slow and gradual process, lasting from three to fifteen years [10]. Consequently, the motor effects of the condition are often too subtle to be evident, making it difficult to diagnose. One of the most frequent and early manifestations of PD is speech impairment, which is experienced by approximately 90% of people living with the disorder [11; 12; 13; 10]. Speech in people with PD is subject to several vocal impairments, such as tremors, lowered loudness, pitch alteration, hoarseness, imprecise articulation, delayed onset time, and decreased intelligibility [14; 15; 16]. These factors have provided researchers with an array of opportunities to conduct in-depth studies on the subject using signal processing and machine learning. As a method of day-to-day patient assessment, speech recording is an effective and non-invasive tool since it is fast, affordable, and generates objective data. Over the past decade, speech research for PD has garnered increasing interest within the research community. Numerous studies have analyzed speech samples from individuals with PD and healthy controls (HC) to conduct classification and monitoring investigations. These studies can be broadly categorized into those based on ratings provided by trained listeners according to their perception [17; 18; 19; 20; 21; 22], acoustic analysis [23; 24; 25; 26; 27], and/or computational methods involving mathematical modeling with signal processing and machine learning techniques [28; 29; 30; 31; 32; 33; 34]. Computational methods employ distinct sets of acoustic features extracted from various speech samples or tasks.