## 1 Introduction  
Voice analysis has being evolving as a promising approach within the automatic assessment of a number of different pathologies due to non-invasiveness, ease of use, and the anytime anywhere accessibility of voice recording systems [1]. In particular, voice analysis can provide detection of some pathologies by measuring the deviation of selected acoustic parameters, becoming clinical biomarkers, with respect to healthy baselines [2], those pathologies including neurological diseases [3; 4; 5]. In particular, patients with Parkinson's Disease (PD) can manifest as a vocal tremor and speech difficulties in the early stage of disease [6], and subjects suffering from Adductor Spasmodic Dysphonia (ASD) can present with vocal cords spasming on the physiologic voice emission. Objective diagnoses of these diseases are relevant to design appropriate treatment strategies, despite the currently adopted empirical-based methodologies that can suffer from subjectivity, being potentially biased by the knowhow of the examiner, and potentially suffering from inter- and intra-rater reliability issues. Within this frame, one of the most adopted rater scales for assessing the PD severity is the perceptual-based Unified Parkinson's Disease Rating Scale [7], whilst for ASD the diagnosis relies on perceptual evaluation performed by ear by a voice analyst or phoniatrician. Even though the performances of specialist analysts enhance inter-analyst agreement, analysts often dispute each other's findings when rating symptom severity and importance [8]. PD and ASD can cause changes in voice quality, usually qualified as inducing hoarseness, strain, and reduced vocal control, as well as a rough and/or \"wet\" quality associated to the vocal emission. These shared symptoms, along with a subjective and unpredictable manifestation and severity of symptoms, make it difficult to differentiate between the two conditions based solely on vocal characteristics. Accurate PD and ASD diagnoses require expertise from healthcare professionals, such as neurologists and otolaryngologists. However, considering that currently there is a lack of widely accepted objective measures or biomarkers specific to one or the other disease, clinicians often act empirically, evaluating multiple factors that include, but are not limited to, a detailed medical history, a physical examination, and a perceptual analysis. With these challenges, a comprehensive evaluation by medical professionals experienced in both movement disorders and voice disorders is crucial for accurate diagnosis and monitoring of PD and ASD [9; 10]. Conveniently, voice signals, which contain some of the most crucial information for detecting and/or isolating PD or ASD can be analyzed automatically and non-invasively by algorithmic means. Artificial Intelligence (AI) voice analysis relies on the extraction of specific features from the vocal signals, and on the classification performed by means of Machine Learning (ML) algorithms [5; 11; 12; 13]. AI-enhanced voice analysis has been used with promising degrees of success for the identification or even staging of various voice-impairing pathological conditions, such as pulmonary diseases and COVID-19 [14; 15], tremors and Parkinsonism [16], and even psychological assessments such as emotion recognition [17; 18; 19], and can be employed to perform a multi-class analysis for preliminarily identifying a patient's pathology. This paper aims to explore AI methodologies for voice analysis based on acoustic features, as clinically relevant biomarkers, for the identification of PD vs. ASD vs. healthy conditions (gathered by control subjects). From validated PD and ASD vocal data we extracted a large number of acoustic features to avoid biasing from specific subsets. This is because, in general, voice analysis for PD detection often relies on some specific prosodic features like the fundamental frequency (F0), jitter, shimmer, Ssignal-to-noise ratio, or mel-frequency cepstral coefficients (MFCCs).