# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 5 Discussion  
Table 4 shows the research works reviewed addressing different aspects of improving automatic speech recognition (ASR) and dysarthria severity classification through deep learning techniques. In [82], the model achieved a character error rate (CER) of 15% and a word error rate (WER) of 59% on a dataset of 2797 recordings, surpassing other models like 4 xLSTM and 3 xCNN-3 xLSTM. Similarly, ref. [81] explored severity classification using MFCC combined with i-vectors in a DNN, achieving 93.97% accuracy in speaker-dependent scenarios. However, the performance dropped to 49.22% in speaker-independent cases, highlighting the challenge of variability in dysarthric speech. These studies emphasize the potential of DL in assistive technologies, targeting either improved speech recognition or severity classification for dysarthric patients. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Methods** & **TP** & **FN** & **TN** & **FP** & **Sensitivity** & **Specificity** & **Bal. Accuracy** \\ \hline ADOMS & 72.4 \(\pm\) 4.195 & 20.6 \(\pm\) 4.195 & 1397.3 \(\pm\) 17.366 & 54.7 \(\pm\) 17.366 & 0.778 \(\pm\) 0.045 & 0.962 \(\pm\) 0.012 & 0.870 \(\pm\) 0.022 \\ Assembled & 71.6 \(\pm\) 3.950 & 21.4 \(\pm\) 3.950 & 1414.7 \(\pm\) 11.833 & 37.3 \(\pm\) 11.833 & 0.770 \(\pm\) 0.042 & 0.974 \(\pm\) 0.008 & 0.872 \(\pm\) 0.021 \\ SMOTE & 76.8 \(\pm\) 3.084 & 16.2 \(\pm\) 3.084 & 1387.7 \(\pm\) 33.012 & 64.3 \(\pm\) 33.012 & 0.826 \(\pm\) 0.033 & 0.956 \(\pm\) 0.023 & **0.891 \(\pm\) 0.015** \\ CE SMOTE & 72.6 \(\pm\) 4.274 & 20.4 \(\pm\) 4.274 & 1407.0 \(\pm\) 8.083 & 45.0 \(\pm\) 8.083 & 0.781 \(\pm\) 0.046 & 0.969 \(\pm\) 0.006 & 0.875 \(\pm\) 0.023 \\ Cluster & 74.0 \(\pm\) 4.595 & 19.0 \(\pm\) 4.595 & 1396.5 \(\pm\) 15.580 & 55.5 \(\pm\) 15.580 & 0.796 \(\pm\) 0.049 & 0.962 \(\pm\) 0.011 & 0.879 \(\pm\) 0.025 \\ SDOMTE & 75.0 \(\pm\) 3.712 & 18.0 \(\pm\) 3.712 & 1395.2 \(\pm\) 16.665 & 56.8 \(\pm\) 16.665 & 0.806 \(\pm\) 0.040 & 0.961 \(\pm\) 0.011 & **0.884 \(\pm\) 0.018** \\ DE oversampling & 61.4 \(\pm\) 3.893 & 31.6 \(\pm\) 3.893 & 1096.1 \(\pm\) 71.286 & 355.9 \(\pm\) 71.286 & 0.660 \(\pm\) 0.042 & 0.755 \(\pm\) 0.049 & **0.708 \(\pm\) 0.023** \\ G-SMOTE & 72.2 \(\pm\) 3.938 & 20.8 \(\pm\) 3.938 & 1416.6 \(\pm\) 10.298 & 35.4 \(\pm\) 10.298 & 0.776 \(\pm\) 0.042 & 0.976 \(\pm\) 0.007 & 0.876 \(\pm\) 0.020 \\ Gaussian & 61.3 \(\pm\) 3.592 & 31.7 \(\pm\) 3.592 & 1376.6 \(\pm\) 38.549 & 75.4 \(\pm\) 38.549 & 0.659 \(\pm\) 0.039 & 0.948 \(\pm\) 0.027 & 0.804 \(\pm\) 0.019 \\ SMOTE & 70.0 \(\pm\) 2.055 & 23.0 \(\pm\) 2.055 & 1408.4 \(\pm\) 21.593 & 43.6 \(\pm\) 21.593 & 0.753 \(\pm\) 0.022 & 0.970 \(\pm\) 0.015 & 0.861 \(\pm\) 0.013 \\ LVQ SMOTE & 77.3 \(\pm\) 4.084 & 15.7 \(\pm\) 4.084 & 1312.1 \(\pm\) 53.272 & 139.9 \(\pm\) 53.272 & 0.831 \(\pm\) 0.044 & 0.904 \(\pm\) 0.037 & 0.867 \(\pm\) 0.024 \\ NDO & 80.9 \(\pm\) 3.725 & 12.1 \(\pm\) 3.725 & 1384.8 \(\pm\) 18.220 & 67.2 \(\pm\) 18.220 & 0.870 \(\pm\) 0.040 & 0.954 \(\pm\) 0.013 & **0.912 \(\pm\) 0.019** \\ Sampling & 65.0 \(\pm\) 4.447 & 28.0 \(\pm\) 4.447 & 1395.6 \(\pm\) 13.737 & 56.4 \(\pm\) 13.737 & 0.699 \(\pm\) 0.048 & 0.961 \(\pm\) 0.009 & 0.830 \(\pm\) 0.022 \\ NEATER & 70.9 \(\pm\) 3.178 & 22.1 \(\pm\) 3.178 & 1400.7 \(\pm\) 15.924 & 51.3 \(\pm\) 15.924 & 0.762 \(\pm\) 0.034 & 0.965 \(\pm\) 0.011 & 0.864 \(\pm\) 0.020 \\ SMODB & 67.1 \(\pm\) 6.208 & 25.9 \(\pm\) 6.208 & 963.8 \(\pm\) 74.754 & 488.2 \(\pm\) 74.754 & 0.722 \(\pm\) 0.067 & 0.664 \(\pm\) 0.051 & **0.693 \(\pm\) 0.030** \\ Supervised & 7 Other studies, like [78], focus on innovative ASR techniques, with the former utilizing automated neural architecture search and visual recognition to achieve a WER of 25.21%, and the latter introducing voicegrams to achieve 64.71% word recognition accuracy. In [80], the authors found that CNN and DNN models significantly outperformed traditional approaches, reaching 96.18% accuracy on the TORGO database and 93.24% on UA-Speech.