## 5 Architecture of the Proposed System
### Feature Extraction
The estimation of the extent of pathological changes in vibrato is based on the observation that for healthy voices vibrato lies at low frequency, while for PD patients it is characterized by presence of high-frequency components [83]. Figure 2: Voice waveforms of the sustained phonation of the vowel /a/ (**A**) and contour of the fundamental frequency (**B**) corresponding to patients with PD (**top**) and patients with non-PD (**down**). Voice analysis employs similar parameters, such as the harmonics-to-noise ratio (HNR) and the noise-to-harmonics ratio (NHR). Their main objective is to detect uncontrolled movements of the vocal folds and their incomplete closure. This parameter is associated with the perception of roughness and hoarseness of the voice [45]. The physiological motivation for this group is that incomplete vocal fold closure leads to the creation of aerodynamic vortices which result in increased acoustic noise. The evaluation between the two components reflects the efficiency of speech, i.e., the greater the flow of air expelled from the lungs into the energy of the vibration of the vocal cords. In these cases, the HNR will be greater. A voice sound is thus characterized by a high HNR, which is associated with a sonorant and harmonic voice. A low HNR denotes an asthenic voice and dysphonia [45]. Some researchers try to determine the threshold of these parameters in order to unambiguously classify pathological voices. Unfortunately, many factors mean that there is no clear boundary that allows for the identification of a pathological voice. A person's gender, the way a person used their tongue on a given day, the sound pressure level, the highly variable acoustic environment, and a number of other aspects make it difficult to develop standards that we can use [40; 83]. For example, according to [PERSON] et al. [41] and [PERSON] et al. [42], NHR is higher for PD patients compared to healthy controls. One of the common voice symptoms in patients with PD is impaired control of the pitch of the voice during prolonged phonation. It is difficult to distinguish natural healthy tone shifts and dysphonic shifts caused by PD using classic parameters. One of the reasons regards the extent to which natural variation is related to the average voice pitch of the subject. Speakers with naturally high-pitched voices will have much larger vibrato and microtremor than those with low-pitched voices, when these variations are measured on an absolute frequency (in hertz) scale. For this reason, the authors of [84] introduced a new measure of dysphonia (pitch period entropy) (PPE), a robust measure sensitive to observed changes in speech specific to PD [85]. The entropy of this probability distribution [43] then characterizes the extent of (non-Gaussian) fluctuations in the sequence of relative semitone pitch period variations. An increase in this entropy measure better reflects the variations  
Figure 3: Representation of jitter and shimmer perturbation measures in speech signal. over and above natural variations in pitch observed in healthy speech production [84]. It is robust to many uncontrollable confounding effects including noisy acoustic environments and normal, healthy variations in voice frequency. Another method to estimate \(F_{0}\) contour-based parameters is PFR (phonatory_frequency_range) [86]. It is defined as the semitone difference between the lowest (f\({}_{0}\) low) and highest (f\({}_{0}\) high) fundamental frequencies [87]. The final parameter, PPF (pitch_perturbation_factor), only describes the voiced part of speech [88]. The numerator and denominator represents the pitch values greater that the given threshold and the total number of extracted pitch values, respectively. \[PPF=\frac{N_{p\geq\textit{treshold}}}{N}\times 100 \tag{2}\]  
Non-standard methods in combination with traditional methods can be optimal and robust in their ability to separate healthy subjects from PD subjects. B.