A two-step approach to leverage contextual data: speech recognition in air-traffic communications

TL;DR

This paper presents a two-step method combining ASR and NLP with surveillance data to significantly enhance callsign recognition accuracy in air-traffic communication, thereby improving safety and efficiency.

Contribution

The paper introduces a novel two-step approach that leverages both speech recognition and natural language processing, integrated with surveillance data, to boost callsign recognition in air traffic communication.

Findings

Achieved up to 53.7% absolute improvement in callsign recognition

Realized 60.4% relative enhancement in recognition accuracy

Demonstrated effectiveness of combining ASR, NLP, and surveillance data

Abstract

Automatic Speech Recognition (ASR), as the assistance of speech communication between pilots and air-traffic controllers, can significantly reduce the complexity of the task and increase the reliability of transmitted information. ASR application can lead to a lower number of incidents caused by misunderstanding and improve air traffic management (ATM) efficiency. Evidently, high accuracy predictions, especially, of key information, i.e., callsigns and commands, are required to minimize the risk of errors. We prove that combining the benefits of ASR and Natural Language Processing (NLP) methods to make use of surveillance data (i.e. additional modality) helps to considerably improve the recognition of callsigns (named entity). In this paper, we investigate a two-step callsign boosting approach: (1) at the 1 step (ASR), weights of probable callsign n-grams are reduced in G.fst and/or in the decoding FST (lattices), (2) at the 2 step (NLP), callsigns extracted from the improved recognition outputs with Named Entity Recognition (NER) are correlated with the surveillance data to select the most suitable one. Boosting callsign n-grams with the combination of ASR and NLP methods eventually leads up to 53.7% of an absolute, or 60.4% of a relative, improvement in callsign recognition.