## 1 Introduction
As a result, inconspicuous techniques to monitor depression symptoms in daily life could be quite useful in diagnosing and screening PD and determining whether or not it requires professional treatment [19]. Speech therefore must be constantly reevaluated to improve the patients' quality of life; therefore, PD-impacted speech must be monitored and screened on a regular basis. Deterioration is linked to swallowing problems in PD [20], which in turns raises the possibility of choking, aspiration pneumonia, and untimely death. Swallowing problems are often underreported by patients with PD, not least related to the lack of suitable screening methods. Monitoring for speech deterioration with a simple and even remotely available acoustic analysis method could serve as a tool to schedule a visit to the doctor. Speech disorders impair patients' capacity to communicate, as they may talk slowly and cannot convey themselves effectively. Their speech is sometimes breathy and mumbled towards the end of a phrase. As a result, individuals are unable to express emotions when speaking. It also has an impact on their socialization capacity, a case that has been especially pronounced during and after the COVID-19 pandemic [21]. Healthy persons may alter their voices to produce a variety of sounds that require delicate coordination and control of the articulatory and respiratory muscles. Patients with PD, on the other hand, have impaired neuromotor control, which affects the vocal mechanism and, as a result, the sounds generated. Speech analysis by machines might help in early identification of PD, however there are large inter- and intra-individual differences, making this difficult. Recent development of speech-based PD screening tools based on speech analysis show the acceptable classification rates in distinguishing between healthy participants and PD patients. However, in these investigations, the data used to build the classification model included voice recordings from both early and late-stage PD patients with varying degrees of speech impairment, resulting in unrealistic results for real-life application cases. In a more realistic scenario, healthy participants or early PD patients with moderate speech impairment will employ an early screening method in questionable situations [22]. The study [23] showed that vocal tract length acquired from phoneme recording using a smartphone may reliably identify persons with [PERSON]'s disease. Our study's goal was to discover anomalies in the PD-affected voice and develop an automated screening method capable of distinguishing between the voices of PD patients and healthy individuals while also providing a voice quality score. We look at whether speech recordings might be utilized as a simple, low-cost method of PD assessment and screening, using deep learning for expert score prediction and evaluation. Acoustic investigations enable high-throughput screening that, if the screening results are aberrant, can be followed by a comprehensive medical evaluation. We developed an automated screening method that can discriminate between the voices of patients with [PERSON]'s disease and healthy participants, as well as provide a voice quality score, to detect anomalies in the voice impacted by [PERSON]'s disease. The main challenge was getting the algorithm to work with different forms of voice input. The Italian PD speech samples (training material) used sustained vowel phonation, while the Lithuanian samples used phonetically balanced phrase analysis. However, because sustained vowel phonation is a type of phonation that is manufactured, that feature establishes the same limitation in the meaning of the sustained vowel signal analysis. Connected speech, on the other hand, is more common in ordinary conversation and may be considered more \"ecologically legitimate\".