# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 4 Experiment Results and Discussion
### Preliminary Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features/Model** & **KNN\%** & **RF\%** & **SVM\%** & **LR\%** & **CNN\%** & **LSTM\%** & **BiLSTM\%** & **BiLSTM\%** \\  & & & & & & & & \\ \hline MFCC & 74 & 85 & 50 & 42 & 88.99 & 77.58 & 91.49 & 92.08 \\ SCC & 65 & 69 & 23 & 22 & 67.29 & 64.30 & 73.93 & 76.06 \\ WMFCC & 79 & 88 & 53 & 43 & 93.27 & 83.96 & 93.72 & 97.17 \\ MFCC + & & 75 & 87 & 54 & 47 & 89.30 & 70.72 & 90.42 & 93.82 \\ WMFCC + SCC & 77 & 89 & 65 & 53 & 94.13 & 84.00 & 94.63 & **98.48** \\ MFCC + WMFCC & 78 & 88 & 78 & 67 & 93.05 & 80.26 & 94.30 & 96.66 \\ MFCC + WMFCC + & & 89 & 80 & 69 & 93.00 & 79.41 & 94.16 & 95.75 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Command classification rates of the four models using 40 MFCC, 40 WMFCC, and 10 SSCFC coefficients as an input (with data augmentation, DL models are trained for 40 epochs. Model-performance metric: Validation accuracy). serve to differentiate each audio sample. Moreover, utilizing this combination requires fewer coefficients, which will explicitly reduce the computational resources. Table 3 presents the results of the models after data augmentation. We used the same setting as before but with the augmented dataset. The best result was achieved through a combination of four factors. Firstly, the increase in feature dimensionality enabled most models to capture additional characteristics from the sound audio. Secondly, dataset augmentation played a crucial role in enhancing model performance and reducing overfitting. Thirdly, the combination of WMFCC and SSCFC features as input contributed significantly. Finally, the effectiveness of our built architecture, BiLSTM-CNN, outperformed the other three classifiers, achieving a validation accuracy of 98.48%. Figure 9 presents the learning curve of the best model (BILSTM-CNN). Considering the dataset size and the characteristics of command records (e.g., some commands containing three words, as shown in Table 1), along with the fact that 50% of the audios in the dataset were recorded in a noisy environment, our model performed well, achieving notable results of 98.48% in validation accuracy. Figure 10 presents a comparison between the BiLSTM-CNN and the fine-tuned Wav2 Vec model based on their performance in different environments: overall, clean, and noisy. Both models showed strong performance in a clean environment, with BiLSTM-CNN slightly outperforming Wav2 Vec (99.0% versus 96.27%). However, the difference in performance becomes more pronounced in noisy environments. The BiLSTM-CNN model maintained a high accuracy of 97.90%, while Wav2 Vec dropped significantly to 86.05%. Furthermore, the BiLSTM-CNN model outperformed Wav2 Vec in overall accuracy, achieving 98.48% compared to Wav2 Vec's 91.16%. This indicates that the BiLSTM-CNN model is more effective at generalizing across different environments, making it a more robust option for the ASR of Moroccan Arabic commands. Several factors contribute to the superior performance of the BiLSTM-CNN model over the Wav2 Vec model in Moroccan Arabic command recognition. The Wav2 Vec model was primarily trained on MSA datasets, which differs significantly from Moroccan Arabic regarding phonetic and lexical properties. The Moroccan Arabic dialect has unique phonemes that do not exist in other Arabic dialects. Because the Wav2 Vec model has not been exposed to these specific phonemes during training, it struggled to recognize commands correctly in this dialect. Therefore, the lack of extensive, high-quality training data  
Figure 9: The learning curve of the proposed model BiLSTM-CNN (best model) using WMFCC and SSC as input achieves a validation accuracy of 98.48%. specific to Moroccan Arabic led to poorer performance in this context. Moreover, as shown in Figure 8, some commands are phonetically similar, especially in noisy environments. The Wav2 Vec model struggled to differentiate between such commands, contributing to its lower performance than the BiLSTM-CNN model.