# Noninvasive Deep Learning Analysis for Smith-Magenis Syndrome Classification
## 5 Discussion
Lastly, an evaluation of pretrained CNN models, with ResNet 50 achieving the highest accuracy of 98.21%, was conducted in [83]. Collectively, these works highlight the growing effectiveness of DL models for dysarthric speech recognition and severity assessment, driving progress in assistive technologies for individuals with speech disabilities. This study utilized an original dataset comprising voice recordings from Spanish-speaking patients diagnosed with SMS, collected from associations and schools. Due to the scarcity of Spanish-speaking SMS patient samples, various oversampling and undersampling techniques were implemented to balance the datasets. By training a CNN and extracting acoustic features such as MFCC and CPP, a highly competitive classification rate of nearly 92% was achieved. These results demonstrate the potential of deep learning techniques for detecting SMS through voice analysis, even in scenarios with exogenous data. A discussion of the three case studies conducted is presented below. In the SC1, SMOTE generates synthetic data points within the feature space by interpolating between existing minority class data points. Furthermore, SMOTE endeavors to grasp the fundamental data distribution and potentially introduce fresh insights to the minority class. Conversely, ROS duplicates existing minority class data points with replacement, effectively augmenting the representation of the minority class within the dataset. Additionally, it merely duplicates existing data points, which could result in overfitting and heightened variability. The aforementioned distinctions between these techniques can manifest in subtle variations within the results, as evidenced by Table 1. However, when applied to the current dataset, these variations are not statistically significant. In the SC2, NearMiss and RUS diverge in their approaches to selecting majority class instances for retention or removal when addressing class imbalance. NearMiss prioritizes retaining instances closest to the decision boundary, aiming to preserve informative examples that may influence classification. Conversely, RUS employs a random selection process, \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Authors** & **Datasets** & **Features** & **Deep Learning Models** & **Accuracy Rate** \\ \hline [PERSON] et al. [82] & 2797 Russian recordings of RHDD speech & MFCC & CTBNet, 4 xLSTM, 3 xCNN + 3 xLSTM & CER: 15\%, WER: 59\%, 2 CR: 36\%, WER: 66\%, 37\%, WER: 63\% \\ \multirow{3}{*}{[PERSON] and [PERSON] [81]} & \multirow{3}{*}{UA-Speech y TORGO} & MFCC, CQCC, & Speaker-Dependent: \\  & & prosodic, glottal, and articulatory features, i-vectors & DNN, CNN, GRU, 1 STM, i-vectors & 93.97\% UA-Speech/ Speaker-Independent: \\  & & MFCC, Mel-scale filter banks, delta features, pitch & DNN, TDNN, BLSTM, 25.21\% WER \\ \multirow{3}{*}{[PERSON] [79]} & \multirow{3}{*}{UA-Speech} & \multirow{3}{*}{Voicegrams, synthetic dysarthric speech} & \multirow{3}{*}{2D CNN} & \multirow{3}{*}{64.71\% WRA} \\  & & MFCC y derivates (39 characteristics) & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \cline{1-1} \cline{5-5}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparative analysis of prior studies on dysarthria, detailing their datasets, features, and employed deep learning models. simply removing majority class instances until a balanced class distribution is achieved. NearMiss prioritizes retaining normative class instances closest in distance (similarity) to SMS class instances. This rationale aims to preserve informative examples residing near the decision boundary, as they hold a higher risk of misclassification.