## 3 Materials and Methods
### U-Lossian Deep Learning Network  
We sought a customized strategy since one of the limitations of traditional loss function implementation is that it equally weights false positive (FP) and false negative (FN) identification. In practice, this leads to voice segmentation maps with great accuracy but low recall. FN detections must be weighted higher than FPs to improve the recall rate for extremely imbalanced data and smaller ROIs, such as the small Lithuanian [PERSON] dataset. Another problem with typical loss functions is that they have difficulties segmenting small ROIs since they do not contribute much to the loss. To address this, we propose using a custom loss function that is parametrized to control the difference between easy background and tough ROI training samples. To focus on speech samples recognized with lesser probability, we propose applying a parameter that exponentiates the cross-entropy loss. We perform fast Fourier transformation (FFT) to the vocalized audio sample with 201 bins to build the spectrogram. Mel-frequency cepstral coefficients (MFCCs) are then calculated using 40 mel scale coefficients from the FFTs, as recommended by Ref. [78]. As a consequence, an MFCC picture with a height of 40 pixels and a length equal to the audio duration is produced. The implementation flow is represented in Figure 1 and the process sequence diagram is given in Figure 2. A doctor has two possibilities when using the PD screening application: (1) utilize a previously recorded audio file, or (2) record live. The signal is then converted to a waveform (any system compatible codec is permitted as it has no discernible effect on classification effectiveness). The signal is then preprocessed for 16 kHz, which speeds up the computation, and is then transformed to mono. The unique characteristics indicating the logarithmic perception of a PD patient's unique intensity and tone were extracted using MFCC. The actions for assessing MFCC features must be conducted in the sequence of each speech signal, and then these characteristics were used to train the CNN algorithm, which also extracts features. Within each vocal outline, a feature vector was saved to hold the features related to the specific spectrogram \"frame\". This also aided in reducing the complexity of the model and achieving improved recognition accuracy. The doctor receives a result along with numerous calculated voice parameters. Figure 1: Implementation flow. To achieve a better balance of accuracy and recall in the processed voice, we presented a modified Hybrid Mask U-Net architecture with an adaptive custom loss function (further named U-lossian). Our approach was inspired by the popular architectures Mask U-Net (often used for \"segmentation\" of sounds, e.g., [79]) and Resnet18 (often used for \"classification\" of sounds, e.g., [80]). We have aimed for an architecture design to operate well with a modest number of training samples, making it appropriate for less popular language datasets such as Lithuanian. The network is made up of two paths: one that  
Figure 2: Process sequence diagram (Symbol ‘X’ depicts the end of the sequence). contracts to extract locality characteristics and one that expands to resample the image maps with contextual information. Skip connections are used to integrate high-resolution local characteristics with low-resolution global features in order to get more relevant results. During our research, we noticed that while training a negative gradient over-suppression, for example, each positive sample of one class in softmax or sigmoid cross-entropy might be interpreted as a negative sample for other classes, resulting in more suppressed gradients for tail classes. To prevent gradient over-suppression, we developed a custom loss function with better regularization. It may also be used to compare the expected and actual sampling frequencies of each class, and then use the division of these frequencies to re-weight loss values for different classes. The custom loss function prioritizes less accurate misclassified predictions.