Accent Adaptation for the Air Traffic Control Domain

Automated speech recognition (ASR) plays a significant role in training and simulation systems for air traffic controllers. However, because English is the default language used in air traffic control (ATC), ASR systems often encounter difficulty with speakers’ non-native accents, for which there is a paucity of data. This paper examines the effects of accent adaptation on the recognition of non-native English speech in the ATC domain. Accent adaptation has been demonstrated to be an effective way to model under-resourced speech, and can be applied to a variety of models. We use Subspace Gaussian Mixture Models (SGMMs) with the Kaldi Speech Recognition Toolkit to adapt acoustic models from American English to German-accented English, and compare it against other adaptation methods. Our results provide additional evidence that SGMMs can be an efficient and effective way to approach this problem, particularly with smaller amounts of accented training data.