## 5 Architecture of the Proposed System
### Feature Extraction
Spectral flatness is the ratio of the geometric mean to the arithmetic mean of the spectrum. The task of this descriptor is to capture the presence of a large number of peaks in the spectrum. It will assume larger values for sounds showing harmonics or consisting of many distinct individual tones. Spectral kurtosis is a descriptor that defines the measure of the flatness of the spectrum distribution around its mean value [92]. Statistically, it is a fourth-order moment. Spectral entropy gauges the peakiness of the spectrum. The spectral rolloff point is a descriptor that determines the frequency below which 95% of the signal energy is contained. Spectral skewness gives a measure of the spectrum distribution asymmetry around its mean value. The spatial slope has been used in speech analysis, especially in modeling speaker stress [93]. The slope is directly related to the resonant characteristics of the vocal folds. Spectral spread calculates the standard deviation around the spectral centroid and represents the \"instantaneous bandwidth\" of a spectrum. Spectral crest measures the relationship between the highest point in a spectrum and the average value of the spectrum. Spectral decrease measures the extent of the spectrum decrease, focusing on the slopes of the lower frequencies. Spectral rollof point measures the bandwidth of the audio signal by determining the frequency bin under which a given percentage of the total energy exists. Spectral flatness calculates the ratio between the geometric mean and the arithmetic mean of a spectrum. Spectral flux measures the variability of a spectrum over time. Spectral kurtosis quantifies the flatness or non-Gaussianity of a spectrum around its center frequency [94]. The last articulation assessment stage involves parameters that measure the range of sonority of the speech signal. A healthy person with a physiological voice, who utters a prolonged vowel, should not have problems with maintaining phonation for a longer period. However, some people with a disease-weakened voice may find it difficult to perform such a task. PD patients produce abnormal unvoiced sounds and have difficulty beginning and/or to stopping vocal fold vibration [39]. The voice signal is described using parameters that determine the so-called voice signal sonorousness in order to investigate if, and to what degree, the examined person experiences this issue. Figure 4: The contour of the formants corresponding to PD patients (**A**) and non-PD patients (**B**). These include the following:  
* Fraction of locally unvoiced pitch frames, which defines which part of the analyzed speech signal is unvoiced;
* Sonorousness coefficient, which is the quotient of voiced to unvoiced frames in the analyzed speech signal. The fraction of locally unvoiced pitch frames is detected by the \(p_{p}\) level described in Section 5.1. The sonorousness coefficient, which is the quotient of voiced to unvoiced frames, is defined as follows:  
\[\text{sonorousness coefficient}=\frac{\text{number of voiced speech signal frames}}{\text{number of all speech signal frames}} \tag{3}\]  
To detect voiced frames of speech, the autocorrelation function with the \(p_{v}\) level indicated in Section 5.1 has been used. Our preliminary research study determined that the difference in this descriptor is caused primarily by weaker operation of the vocal folds, as well as by the stiffness of the oral cavity, pharynx and larynx muscles, in those patients [95]. Furthermore, it can be caused by the issue of generating voiced consonants like \"b\" and inaccurate articulation in patients with PD [3].