DIAL Project Results for Recognition of Seldom Airport Dependent Words

Kurzfassung

Thanks to Alexa, Siri or Google Assistant automatic speech recognition (ASR) has changed our daily life during the last decade. Prototypic applications in the air traffic management (ATM) domain are available. Recently pre-filling radar label entries by ASR support has reached the technology readiness level before industrialization (TRL6). However, seldom spoken and airspace related words relevant in the ATM context remain a challenge for sophisticated applications. Open-source ASR toolkits or large pre-trained models for experts – allowing to tailor ASR to new domains – can be exploited with a typical constraint on availability of certain amount of domain specific training data, i.e., typically transcribed speech for adapting acoustic and/or language models. In general, it is sufficient for a “universal” ASR engine to reliably recognize a few hundred words that form the vocabulary of the voice communications between air traffic controllers and pilots. However, for each airport some hundred dependent words that are seldom spoken need to be integrated. These challenging word entities comprise special airline designators and waypoint names like “dexon” or “burok”, which only appear in a specific region. When used, they are highly informative and thus require high recognition accuracies. A plug and play customization with a minimum expert manipulation framework was developed by Idiap which is based on Lattice Rescoring and GBoosting. This report evaluates the performance of the approach with respect to the special words on different test data set from 6 different air traffic controllers being recording in simulation environment of DLR in August and September 2023 in the context of the DIAL project. Although the word error rate (WER) for the special airport dependent waypoint words is still very high wth 85% to 95%, the new approach has improved the overall word recognition performance, because nearby words like “direct“ or “proceed“ are now recognized much better.