N-best list re-ranking using syntactic score: A solution for improving speech recognition accuracy in air traffic control

Abstract

Recently, a lot of research has been conducted to bring Automatic Speech Recognition (ASR) into various areas of Air Traffic Control (ATC), such as ATC simulation and training, monitoring live operators for with the aim of safety improvements, ATC workload measurement and conducting analysis on large quantities of controller-pilot speech. Due to the high accuracy requirements of the ATC context and its unique challenges, ASR has not been widely adopted in this field. In this paper, in order take advantage of the opportunities offered by the ATC context such as standardized phraseology and small vocabulary size to reduce the Word Error Rate (WER) of ASR in ATC, we perform n-best list re-ranking using syntactic knowledge. We propose a novel feature called syntactic score which is computed using syntactic rules. We also propose a WER-Sensitive Pairwise Perceptron algorithm and use the perceptron to combine the proposed feature with the decoder's confidence score. We integrate the model into the Pocketsphinx speech recognizer and evaluate the model in terms of Word Error Rate (WER) on the well known ATCOSIM and our own ATCSC corpora. The results shows that our proposed approach reduces 1.21% and 0.21% WER on the ATCSC and ATCOSIM corpora respectively.