Semi-supervised Learning with Semantic Knowledge Extraction for Improved Speech Recognition in Air Traffic Control

Abstract

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.