Automatic Speech Recognition Benchmark for Air-Traffic Communications

Advances in Automatic Speech Recognition (ASR) over the lastdecade opened new areas of speech-based automation such as inAir-Traffic Control (ATC) environments. Currently, voice communicationand data links communications are the only wayof contact between pilots and Air-Traffic Controllers (ATCo),where the former is the most widely used and the latter is anon-spoken method mandatory for oceanic messages and limitedfor some domestic issues. ASR systems on ATCo environmentsinherit increasing complexity due to accents from non-English speakers, cockpit noise, speaker-dependent biases andsmall in-domain ATC databases for training. Hereby, we introduceCleanSky EC-H2020 ATCO2, a project that aims todevelop an ASR-based platform to collect, organize and automaticallypre-process ATCo speech-data from air space. Thispaper conveys an exploratory benchmark of several state-ofthe-art ASR models trained on more than 170 hours of ATCospeech-data. We demonstrate that the cross-accent flaws dueto speakers accents are minimized due to the amount of data,making the system feasible for ATC environments. The developedASR system achieves an averaged word error rate (WER)of 7.75% across four databases. An additional 35% relative improvementin WER is achieved on one test set when training aTDNNF system with byte-pair encoding.