# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 4 Experiment Results and Discussion
### Preliminary Results  
After the dataset construction, splitting, and feature extraction, we applied the standardization scaling method to transform the dataset, aiming to enhance the stability and performance of the model. Furthermore, to establish an initial baseline for the classification complexity, we tested the eight classifiers using only 13-dimensional MFCC coefficients as input with a minimum number of model hyper-tuning. Table 1 presents the results of the first experiment on the dataset. In this preliminary experiment, our proposed architecture BiLSTM-CNN achieved the best result, with a validation accuracy of 73.55%. However, this result falls below the expected threshold for consideration. The results suggest that the model struggles to learn distinct command patterns, which is reasonable since many commands share similar rhythm and pitch characteristics, as illustrated in Figure 8. Consequently, the model's performance was adversely affected. Furthermore, evidence of over  
Figure 7: Waveform variation in Command 14 by the same contributor 3. A comparison between Repetitions 1 (**a**) and Repetitions 10 (**b**) reveals a decrease in amplitude from 0.50 (Rep 1) to 0.20 (Rep 10), accompanied by an elevated noise ratio. us to construct a dataset consisting of 16,800 utterances, with 840 utterances representing each command. The use of 40-dimensional MFCCs, instead of the 13 coefficients, contributed to enhancing the performance of both ML and DL models, particularly SVM, CNN, and Bi-LSTM models, as demonstrated in Table 2 (the first rows). For instance, accuracy for CNN improved from 71.44% to 83.33%, and for BiLSTM-CNN, from 73.55% to 81.37%. Furthermore, the data-augmentation process, as shown in Table 3, significantly contributed to enhancing the models' performance, resulting in notable results. After this process, we assessed the performance of the models using different feature-extraction techniques, i.e., 40-dimensional MFCCs, 40-dimensional WMFCCs, and 10-dimensional SSCs, individually and in combinations. To train our models using WMFCC coefficients, we conducted multiple experiments to identify the optimal values for delta and double-delta weights (p and q, as detailed in Section 3.2.2). The optimal values were determined to be p = 0.5 and q = 0.1. Table 2 presents the results of the models before data augmentation. The RF classifier achieved the best result among the ML models, achieving a validation accuracy of 87.79% when utilizing a combination of WMFCC and SCC coefficients. Our proposed architecture yielded the best result among the DL models, achieving a validation accuracy of 93.67% when trained on WMFCC and SCC features. It is noteworthy that across almost all models, the combination of WMFCC and SCC coefficients consistently outperformed other coefficient combinations. This observation underscores the effectiveness of these features in extracting relevant information, including both static and dynamic characteristics, which \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Features/Model** & **KNN\%** & **RF\%** & **SVM\%** & **LR\%** & **CNN\%** & **LSTM\%** & **BiLSTM\%** & **BiLSTM\%** \\  & & & & & & & & \\ \hline MFCC & 57.48 & 70.85 & 52.05 & 45.27 & 83.33 & 55.97 & 79.95 & 81.37 \\ SCC & 42.07 & 55.53 & 26.56 & 24.24 & 58.56 & 41.44 & 58.73 & 62.30 \\ WMFCC & 65.86 & 85.83 & 57.75 & 46.35 & 92.06 & 75.85 & 90.96 & 91.62 \\ MFCC + & 57.66 & 71.66 & 55.08 & 49.73 & 81.46 & 54.19 & 77.90 & 78.07 \\ WMFCC + SCC & **65.33** & **87.79** & 70.94 & 59.36 & **92.16** & **78.52** & **91.05** & **93.67** \\ MFCC + WMFCC & 64.94 & 84.48 & 81.18 & 73.51 & 91.44 & 69.85 & 87.24 & 89.65 \\ MFCC + WMFCC + & 63.99 & 84.76 & **83.96** & **73.53** & 89.75 & 67.02 & 90.46 & 90.55 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Command classification rates of the models using 40 MFCC, 40 WMFCC, and 10 SSCFC coefficients as an input (without data augmentation, DL models are trained for 40 epochs. Model-performance metric: Validation accuracy).