Abstract
This paper addresses the persistent challenges in speech processing within the Air Traffic Control (ATC) domain, a field where despite extensive research, issues such as handling noisy environments, accented speech, and the need for strict adherence to standard phraseology continue to undermine conventional language models. Our study employs a hybrid approach that integrates syntactic analysis with advanced machine learning classification algorithms – Logistic Regression, Lagrangian Support Vector Machine, and Naïve Bayes. By mixing and matching algorithms tailored for specific aspects of speech processing, our approach moves away from traditional reliance on a singular integrated system, illustrating through rigorous testing with the ATCOSIM dataset that such a multifaceted strategy markedly improves command recognition accuracy and adapts more effectively to the unique linguistic features of ATC speech. Results highlight the superior performance of Logistic Regression across various command recognition categories, pointing towards a promising direction for future advancements in ATC speech recognition technologies aimed at reducing human workload and increasing automation precision. This paper explores the complexities of the required analysis techniques and underscores the necessity of employing diverse algorithms in the processing pipeline to enhance overall system accuracy.