Recognising emotions in air-ground communications with deep learning

Abstract

Decision-making in air traffic operations often requires a stable emotional state to ensure the decision quality. To ensure aeronautical decisions are free from emotional impacts, this research presents a novel dataset and deep learning model to identify the emotions of stakeholders, including pilots and air traffic controllers (ATCOs). We recorded 30 utterances in seven different emotions from 20 participants who are pilots or ATCOs. Features were extracted from the utterance recordings for emotion recognition. A long short-term memory (LSTM) model was constructed to perform emotion recognition. The proposed model yielded a test accuracy at 64.29 %. The model demonstrates the potential in identifying emotions of pilots and ATCOs via their communications.