## 5 Architecture of the Proposed System
### Feature Extraction  
The second stage of engineering a conceptual speech signal processing system for the purposes of early diagnosis of neurodegenerative diseases, after pre-processing, is speech signal parameterization, which is an attempt at extracting certain distinguishing descriptors of various representations that may reflect voice changes. As part of this analysis section, we have suggested employing the speech signal generation process. From a technical point of view, a speech signal is formed as a result of the laryngeal tone plexus (phonon) and a vocal tract response (articulation) powered by the lungs [29]. \[s(t)=g(t)*h(t) \tag{1}\]  
\(s(t)\)--resultant speech signal;  
\(g(t)\)--stimulation;  
\(h(t)\)--voice track. Phonation is a process of human voice formation resulting from vocal fold vibrations due to exhaled air, and articulation is a process of shaping human speech sounds, which takes place in the speech apparatus part that includes the vocal tract, i.e., tongue, jaw, and cheek movements. The articulation determines the frequency response. The third mechanism of speech production is prosody, which defines the sound properties of speech, including the phonetic, syllabic and word sequences of utterances [80]. Parkinsonian dysarthria is a multidimensional impairment affecting all different aspects of speech, such as speech respiration, phonation, articulation, and prosody. PD can cause changes in the articulation and phonation of PD patients, wherein increases or decreases occur in some acoustic features [45]. The primary and basic form in which the speech signal is present is its temporal form. This form contains all the elements necessary for analysis and recognition, but the information is convoluted and is not clearly visible. If we want to focus on the specified mechanism of speech production, we have to remove the undesired ingredient by using the signal analysis method for the reconvolution signal (i.e., spectral analysis, cepstral analysis). The experimental part of this study was carried out using an additional Matlab library, which allows for the analysis of acoustic signals in the time and frequency domains. A. Evaluation of the phonation process  
The phonation process is mainly defined by periodicity, noise content, and the phonation process's non-linearity. Periodicity in speech covers the ability to generate a continuous flow of air during the production of vowels with prolonged phonation. To capture vocal fold vibration's temporal and amplitudinal variations, phonation features are derived from voiced segments. The stability and constancy of such a flow through the vocal cords may be characterized in terms of parameters such as amplitude and/or frequency variability. The time interval between successive vocal fold closures determines the smallest repeatable sequence in a speech signal. This interval is called the fundamental period. The inverse of this period determines the fundamental frequency (\(F_{0}\)) and is one of the most important parameters that characterize a voiced speech source. If we want to analyze non-linearity, we have to focus on ways of engaging sound sources in the form of the larynx and vocal tract airways (the filter). This occurs in two fundamentally different ways. The first is linear source-filter coupling, where source frequencies are produced independently of the acoustic pressures in the airways. The glottal airflow in the larynx is produced aerodynamically, with a quasisteady transglottal pressure and a flow pulse that mirrors the time-varying glottal area. The second is non-linear coupling, where the acoustic airway pressures contribute to the production of frequencies at the source. In the non-linear case, the transglottal pressure includes a strong acoustic component, much like in woodwind instruments where the airflow through the reed is driven by acoustic pressures of the instrument bore, or in brass instrument playing, where the lip flow is driven by the acoustic pressures in the brass tube.