1 research outputs found
Semi-supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control
Automatic Speech Recognition (ASR) can introduce higher levels
of automation into Air Traffic Control (ATC), where spoken
language is still the predominant form of communication.
While ATC uses standard phraseology and a limited vocabulary,
we need to adapt the speech recognition systems to local
acoustic conditions and vocabularies at each airport to reach
optimal performance. Due to continuous operation of ATC systems,
a large and increasing amount of untranscribed speech
data is available, allowing for semi-supervised learning methods
to build and adapt ASR models. In this paper, we first identify
the challenges in building ASR systems for specific ATC
areas and propose to utilize out-of-domain data to build baseline
ASR models. Then we explore different methods of data
selection for adapting baseline models by exploiting the continuously
increasing untranscribed data. We develop a basic approach
capable of exploiting semantic representations of ATC
commands. We achieve relative improvement in both word error
rate (23.5%) and concept error rates (7%) when adapting
ASR models to different ATC conditions in a semi-supervised
manner