Publication Details
An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain
Prasad Amrutha (DCGM)
Motlíček Petr, doc. Ing., Ph.D. (DCGM)
ZULUAGA-GOMEZ, J.
NIGMATULINA, I.
Madikeri Srikanth
SCHUEPBACH, C.
speaker clustering; speaker role detection
In air traffic management (ATM), voice communications are critical for ensuring the safe
and efficient operation of aircraft. The pertinent voice communications-air traffic controller (ATCo)
and pilot-are usually transmitted in a single channel, which poses a challenge when developing
automatic systems for air traffic management. Speaker clustering is one of the challenges when
applying speech processing algorithms to identify and group the same speaker among different
speakers. We propose a pipeline that deploys (i) speech activity detection (SAD) to identify speech
segments, (ii) an automatic speech recognition system to generate the text for audio segments,
(iii) text-based speaker role classification to detect the role of the speaker-ATCo or pilot in our
case-and (iv) unsupervised speaker clustering to create a cluster of each individual pilot speaker
from the obtained speech utterances. The speech segments obtained by SAD are input into an
automatic speech recognition (ASR) engine to generate the automatic English transcripts. The speaker
role classification system takes the transcript as input and uses it to determine whether the speech
was from the ATCo or the pilot. As the main goal of this project is to group the speakers in pilot
communication, only pilot data acquired from the classification system is employed. We present a
method for separating the speech parts of pilots into different clusters based on the speaker's voice
using agglomerative hierarchical clustering (AHC). The performance of the speaker role classification
and speaker clustering is evaluated on two publicly available datasets: the ATCO2 corpus and the
Linguistic Data Consortium Air Traffic Control Corpus (LDC-ATCC). Since the pilots' real identities
are unknown, the ground truth is generated based on logical hypotheses regarding the creation of
each dataset, timing information, and the information extracted from associated callsigns. In the case
of speaker clustering, the proposed algorithm achieves an accuracy of 70% on the LDC-ATCC dataset
and 50% on the more noisy ATCO2 dataset.
@article{BUT187753,
author="KHALIL, D. and PRASAD, A. and MOTLÍČEK, P. and ZULUAGA-GOMEZ, J. and NIGMATULINA, I. and MADIKERI, S. and SCHUEPBACH, C.",
title="An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain",
journal="Aerospace",
year="2023",
volume="10",
number="10",
pages="1--14",
doi="10.3390/aerospace10100876",
issn="2226-4310",
url="https://www.mdpi.com/2226-4310/10/10/876"
}