# Efficient and Robust Arabic Automotive Speech Command Recognition System
## 5 Conclusions and Feature Work  
In this paper, we introduce MASCD, our newly constructed Moroccan Arabic speech dataset encompassing 20 of the most commonly used commands in automobiles. Moreover, we designed an end-to-end hybrid deep-learning-based BiLSTM-CNN command classifier and compared it with strong ML and DL models, namely LR, SVM, RF, KNN, CNN, LSTM, and Bi-LSTM. Different feature-extraction techniques were investigated to extract the most pertinent information from the data. Accordingly, MFCC, WMFCC, and SSC features were used and compared, individually and in combination. To establish a baseline understanding of the complexity of the classification task, we conducted an initial experiment using the original dataset (without augmentation). We used 13-dimensional MFCC coefficients as input. In this experiment, all four models achieved a validation accuracy below 73%. Subsequently, we conducted additional experiments using different features and models with the augmented dataset, which led to a performance enhancement of approximately 20% accuracy across the models. The experimental results of this study reveal that combining MFCC with SSC features slightly outperformed MFCC features. On the other hand, combining WMFCC with SSC features significantly outperformed both MFCC and the combination of MFCC SSC. This combination yielded good and robust results, even in noisy conditions as it combines static and dynamic features and adapts well to additive noise, all while using a minimal number of vector dimensions, which implicitly reduces computational complexity. Furthermore, the result shows that our BiLSTM-CNN model outperformed other baseline classifiers and the existing solution in the SOTA, achieving a notable accuracy rate of 98.48%. To our knowledge, this paper is the first research in the field of automotive speech recognition in the Arabic dialect language. Consequently, it establishes a robust foundation for research and innovation to bridge the gap between English and Arabic research. The primary challenge faced in this work was the collection of a natural and representative dataset. For future work, we intend to expand our dataset by incorporating more commands and contributors to create a massive and representative dataset. Moreover, we intend to include all the Arabic dialects by including contributors from other Arabian countries. Additionally, we aim to explore and build more performant architectures using attention mechanisms and transformer architecture due to their promising results in the field of speech recognition. Conceptualization, S.O. and S.E.G. ; methodology, S.O. ; software, S.O. ; validation, S.E.G. ; writing--original draft preparation, S.O. and S.E.G. ; writing--review and editing, S.O. data curation, S.O. ; supervision, S.E.G. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Data Availability StatementData are contained within the article. Conflicts of InterestThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Features/ Article Ref** & **[PERSON] et al. (2009)** & **[PERSON] et al. (2009)** & **[PERSON] and [PERSON]** & **[PERSON] et al.** & **Our Model** \\  & [11] & [11] & **(2022)**[10] & **(2021)**[8] & **Our Model** \\ \hline Dataset Size & 4800 & 4800 & 720 & 12,000 & 5600 \\ \hline Number of & & & & & \\ Commands & 12 & 12 & 5 & 40 & 20 \\ \hline Model used & HMM & HMM \&GMM & SVM & CNN & BiLSTM—CNN \\ \hline Performance & 76\% & 82\% & 95.48\% & 97.97\% & **98.48\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison between state of art command recognition systems in the literature and our proposed system. * (12) [PERSON]; [PERSON] A speech enhancement method based on Kalman filtering.