Date of Award


Document Type


Degree Name

Master of Science in Information Systems and Technology


Information and Decision Sciences

First Reader/Committee Chair

Shayo, Conrad


The need for automatic speech recognition in air traffic control is critical as it enhances the interaction between the computer and human. Speech recognition helps to automatically transcribe the communication between the pilots and the air traffic controllers, which reduces the time taken for administrative tasks. This project aims to provide improvement to the Automatic Speech Recognition (ASR) system for air traffic control by investigating the impact of convolution LSTM model on ASR as suggested by previous studies. The research questions are: (Q1) Comparing the performance of ConvLSTM with other conventional models, how does ConvLSTM perform with respect to recognizing domain-specific terminology and understanding long-range context? (Qn2) How can the ConvLSTM model for ASR be enhanced most effectively by specific training strategies, scalability approaches, and data preprocessing methods? The findings and conclusions are: (Q1) The architecture of the ConvLSTM model performed better than the other convolutional/traditional neural network models. The efficient performance of the ConvLSTM model in handling both spatial and temporal data resulted effective in addressing challenges related to dynamic and multilingual communication environment. The conclusion for (Q2) After successfully implementing the range of transformation processes, the accuracy of the ConvLSTM model substantially improved which resulted in enhanced robustness and accuracy making the model adaptable to various scalability and data processing approach for speech recognition. The conclusion for areas for future studies include exploring the model’s proficiency with different languages and non-native English accents. Researchers can also investigate the model’s effectiveness and accuracy in extreme weather scenarios.