Publication Details
Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems
Nigmatulina Iuliia (IDIAP)
Prasad Amrutha (DCGM FIT BUT)
Motlíček Petr, Ing., Ph.D. (IDIAP)
Veselý Karel, Ing., Ph.D. (DCGM FIT BUT)
Kocour Martin, Ing. (DCGM FIT BUT)
Szőke Igor, Ing., Ph.D. (ReplayWell)
automatic speech recognition, contextual semisupervised learning, air traffic control, air-surveillance data, callsign detection.
Air traffic management and specifically air-traffic control (ATC) rely mostly on voice communications between Air Traffic Controllers (ATCos) and pilots. In most cases, these voice communications follow a well-defined grammar that could be leveraged in Automatic Speech Recognition (ASR) technologies. The callsign used to address an airplane is an essential part of all ATCo-pilot communications. We propose a two-step approach to add contextual knowledge during semi-supervised training to reduce the ASR system error rates at recognizing the part of the utterance that contains the callsign. Initially, we represent in a WFST the contextual knowledge (i.e. air-surveillance data) of an ATCo-pilot communication. Then, during Semi-Supervised Learning (SSL) the contextual knowledge is added by secondpass decoding (i.e. lattice re-scoring). Results show that unseen domains (e.g. data from airports not present in the supervised training data) are further aided by contextual SSL when compared to standalone SSL. For this task, we introduce the Callsign Word Error Rate (CA-WER) as an evaluation metric, which only assesses ASR performance of the spoken callsign in an utterance. We obtained a 32.1% CA-WER relative improvement applying SSL with an additional 17.5% CA-WER improvement by adding contextual knowledge during SSL on a challenging ATC-based test set gathered from LiveATC.
@INPROCEEDINGS{FITPUB12611,
author = "Juan Zuluaga-Gomez and Iuliia Nigmatulina and Amrutha Prasad and Petr Motl\'{i}\v{c}ek and Karel Vesel\'{y} and Martin Kocour and Igor Sz\H{o}ke",
title = "Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems",
pages = "3296--3300",
booktitle = "Proceedings Interspeech 2021",
journal = "Proceedings of Interspeech - on-line",
volume = 2021,
number = 8,
year = 2021,
location = "Brno, CZ",
publisher = "International Speech Communication Association",
ISSN = "1990-9772",
doi = "10.21437/Interspeech.2021-1373",
language = "english",
url = "https://www.fit.vut.cz/research/publication/12611"
}