Detail publikace

Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems

ZULUAGA-GOMEZ, J. NIGMATULINA, I. PRASAD, A. MOTLÍČEK, P. VESELÝ, K. KOCOUR, M. SZŐKE, I.

Originální název

Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

automatic speech recognition, contextual semisupervised learning, air traffic control, air-surveillance data, callsign detection.

Autoři

ZULUAGA-GOMEZ, J.; NIGMATULINA, I.; PRASAD, A.; MOTLÍČEK, P.; VESELÝ, K.; KOCOUR, M.; SZŐKE, I.

Vydáno

30. 8. 2021

Nakladatel

International Speech Communication Association

Místo

Brno

ISSN

1990-9772

Periodikum

Proceedings of Interspeech

Ročník

2021

Číslo

8

Stát

Francouzská republika

Strany od

3296

Strany do

3300

Strany počet

5

URL

https://www.isca-speech.org/archive/interspeech_2021/zuluagagomez21_interspeech.html

BibTex

@inproceedings{BUT175846,
  author="ZULUAGA-GOMEZ, J. and NIGMATULINA, I. and PRASAD, A. and MOTLÍČEK, P. and VESELÝ, K. and KOCOUR, M. and SZŐKE, I.",
  title="Contextual Semi-Supervised Learning: An Approach to Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems",
  booktitle="Proceedings Interspeech 2021",
  year="2021",
  journal="Proceedings of Interspeech",
  volume="2021",
  number="8",
  pages="3296--3300",
  publisher="International Speech Communication Association",
  address="Brno",
  doi="10.21437/Interspeech.2021-1373",
  issn="1990-9772",
  url="https://www.isca-speech.org/archive/interspeech_2021/zuluagagomez21_interspeech.html"
}