An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU150718" target="_blank" >RIV/00216305:26230/23:PU150718 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2226-4310/10/10/876" target="_blank" >https://www.mdpi.com/2226-4310/10/10/876</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/aerospace10100876" target="_blank" >10.3390/aerospace10100876</a>

Result language

angličtina

Original language name

An Automatic Speaker Clustering Pipeline for the Air Traffic Communication Domain

Original language description

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 cluste

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Aerospace

ISSN

2226-4310

e-ISSN

—

Volume of the periodical

10

Issue of the periodical within the volume

10

Country of publishing house

CH - SWITZERLAND

Number of pages

14

Pages from-to

1-14

UT code for WoS article

001095599200001

EID of the result in the Scopus database

2-s2.0-85175153044