Publication Details
BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications
Sarfjoo Seyyed Saeed
Prasad Amrutha (DCGM)
NIGMATULINA, I.
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
Ondřej Karel, Ing. (FIT)
OHNEISER, O.
HELMKE, H.
Text-based speaker diarization, speaker change detection, speaker role detection, air traffic control communications, chunking
Automatic speech recognition (ASR) allows transcribing the communications between air traffic controllers (ATCOs) and aircraft pilots. The transcriptions are used later to extract ATC named entities, e.g., aircraft callsigns. One common challenge is speech activity detection (SAD) and speaker diarization (SD). In the failure condition, two or more segments remain in the same recording, jeopardizing the overall performance. We propose a system that combines SAD and a BERT model to perform speaker change detection and speaker role detection (SRD) by chunking ASR transcripts, i.e., SD with a defined number of speakers together with SRD. The proposed model is evaluated on real-life public ATC databases. Our BERT SD model baseline reaches up to 10% and 20% token-based Jaccard error rate (JER) in public and private ATC databases. We also achieved relative improvements of 32% and 7.7% in JERs and SD error rate (DER), respectively, compared to VBx, a well-known SD system.1
@inproceedings{BUT185192,
author="ZULUAGA-GOMEZ, J. and SARFJOO, S. and PRASAD, A. and NIGMATULINA, I. and MOTLÍČEK, P. and ONDŘEJ, K. and OHNEISER, O. and HELMKE, H.",
title="BERTraffic: BERT-based Joint Speaker Role and Speaker Change Detection for Air Traffic Control Communications",
booktitle="IEEE Spoken Language Technology Workshop, SLT 2022 - Proceedings",
year="2023",
pages="633--640",
publisher="IEEE Signal Processing Society",
address="Doha",
doi="10.1109/SLT54892.2023.10022718",
isbn="978-1-6654-7189-3",
url="https://ieeexplore.ieee.org/document/10022718"
}