Exploring Contextual Knowledge-Enhanced Speech Recognition in Air Traffic Control Communication: A Comparative Study

Accurate recognition of named entities from spoken instructions remains a significant challenge for automatic speech recognition (ASR) techniques in air traffic control (ATC), which limits the reliability of ASR-based applications. A promising solution to overcome this challenge is to integrate prior contextual knowledge into ASR since it contains rich named entities used in ATC communications. Although existing studies have investigated ATC-related contextual ASR techniques, there is a lack of benchmarks to evaluate the advantages of different approaches. In this article, a comprehensive comparative study is presented to explore effective contextual ASR approaches for the ATC domain. Specifically, several typical contextual ASR approaches are introduced in ATC to conduct a comprehensive comparison. Moreover, a novel contextual ASR model, denoted CATCNet, is presented to dedicatedly address the domain-specific problems in ATC, such as limited resources, fast speech, and volatile noise. Several evaluation metrics are proposed to validate the performance of comparison approaches based on the practical requirements of ATC efforts. Extensive experiments are conducted across two real-world ATC speech corpora to build the benchmark. The experimental results demonstrated that integrating context knowledge is effective in improving the recognition performance of named entities. Crucially, the proposed CATCNet outperforms other baseline models by confirming all technical improvements, achieving 80.0% and 86.54% instruction recognition accuracy (IRA) on the ATCSpeech and C-ATCSpeech corpora, respectively. It is believed that this work not only overcomes the bottleneck of ASR performance in the ATC domain, but also provides an applicable solution for ATC-related ASR applications.