## 6 Conclusions  
In this paper, we introduced a method for speech signal processing to illustrate the utility of deep learning-driven voice analysis as a screening tool for Parkinson's disease (PD). We investigated whether voice recordings might be used to provide a simple, low-cost approach to the assessment and screening of Parkinson's disease, using deep learning to predict and evaluate expert scores. As a consequence, we created a Deep U-lossian model, a modified Hybrid Mask U-Net architecture with an adaptive custom loss function, for PD evaluation and detection in order to achieve a better balance of accuracy and recall \begin{table}
\begin{tabular}{l l l} \hline \hline
**Method** & **Accuracy** & **Dataset (Language, Availability)** \\ \hline Sparse kernel transfer learning [59] & 86.72\% & UCI repository v.2013 (English, open on request) [91] \\ Linear DL network [68] & 81.67\% & Telemonitoring voice dataset (Unspecified, closed) \\ Linear DL network + mRMR feature selection [69] & 99.1\% & UCI repository v.2019 (English, open on request) [92] \\ Ensemble classifier [67] & 94.12\% & UCI repository v.2019 (English, open on request) [92] \\ Dual-side learning ensemble [60] & 98.41\% & LSVT voice rehabilitation dataset [93] \\ LSTM [70] & 97.12\% & UCI repository v.2013 (English, open on request) [91] \\
1D CNN [58] & 87.76\% & UCI repository v.2013 (English, open on request) [91] \\ ADCNN [74] & 98.11\% & UCI repository v.2013 (English, open on request) [91] \\ Bidirectional LSTM [71] & 75.56\% & GYENNOLO SCIENCE Parkinson Disease Research Center dataset (Chinese, closed) \\ LSTM with SSWA-based attention [72] & 92.5\% & Proprietary (Indian, closed) \\ ResNet18 [63] & 91.7\% & PC-GITA database (Spanish, open) [94] \\ Alexent [64] & 91.7\% & Dataset of Department of Neurology at the Medical University of Warsaw (Polish, closed) \\ DenseNet161 [65] & 89.75\% & mWPower PD dataset (English, open) [34] \\ CNN-ANN [61] & 93.10\% & [PERSON] dataset (English, open) \\ Ensemble CNN [62] & 97.3\% & PC-GITA database (Spanish, open) [94] \\ CNN with IAIF and QCP [66] & 97.3\% & PC-GITA database (Spanish, open) [94] \\ EDF-EMD [73] & 92.59\% & Dataset+CPPDD (Chinese, closed) \\ Hybrid U-lossian network & 89.64\% & LSMU Lithuanian dataset (Lithuanian, open on request) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of different methods of PD voice analysis. in processed speech. The classification accuracy on two speech corpora (ItalianPVS and Lithuanian voice dataset), 0.8964 and 0.7949, respectively, is satisfactory from a medical aspect, confirming the proposed model's excellent generalizability. Furthermore, the proposed model has demonstrated exceptional performance in terms of speed, ensuring real-time performance. The method has a strong potential to be employed in clinical settings as a sensitivity test for the screening of [PERSON]'s disease-related voice and speech alterations and as a result, the future work of the study will be a proper clinical validation in the hospital environment, with the idea being in line to create a system that could analyze a large number of telephone voice calls (patients calling the Department of Otorhinolaryngology for remote consultation, allowing doctors to acquire live data on a patient while he is still speaking). Conceptualization, R.M. and V.U. ; Data curation, R.D., A.K. and K.P. ; Formal analysis, R.M., R.D., A.K., E.P. and K.P. ; Funding acquisition, V.U. ; Investigation, R.M. and K.P. ; Methodology, R.M., E.P., K.P. and V.U. ; Project administration, R.M. and V.U. ; Resources, V.U. ; Software, A.K. ; Supervision, R.D. and V.U. ; Validation, R.M., R.D., E.P. and V.U. ; Visualization, R.D. and A.K. ; Writing--original draft, R.M., K.P. and V.U. ; Writing--review and editing, R.M. All authors have read and agreed to the published version of the manuscript. This research has received funding from European Regional Development Fund (project No 13.1.1-LMT-K-718-05-0027) under grant agreement with the Research Council of Lithuania (LMTLT).