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Automatic Speech Analysis Framework for ATC Communication in HAAWAII

Motlicek, Petr and Prasad, Amrutha and Nigmatulina, Iuliia and Helmke, Hartmut and Ohneiser, Oliver and Kleinert, Matthias (2023) Automatic Speech Analysis Framework for ATC Communication in HAAWAII. In: 13th SESAR Innovation Days. 13th SESAR Innovation Days, 2023-11-27 - 2023-11-30, Sevilla, Spanien. doi: 10.61009/SID.2023.1.40.

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Over the past years, several SESAR funded exploratory projects focused on bringing speech and language technologies to the Air Traffic Management (ATM) domain and demonstrating their added value through successful applications. Recently ended HAAWAII project developed a generic architecture and framework, which was validated through several tasks such as callsign highlighting, pre-filling radar labels, and readback error detection. The primary goal was to support pilot and air traffic controller communication by deploying Automatic Speech Recognition (ASR) engines. Contextual information (if available) extracted from surveillance data, flight plan data, or previous communication can be exploited via entity boosting to further improve the recognition performance. HAAWAII proposed various design attributes to integrate the ASR engine into the ATM framework, often depending on concrete technical specifics of target air navigation service providers (ANSPs). This paper gives a brief overview and provides an objective assessment of speech processing components developed and integrated into the HAAWAII framework. Specifically, the following tasks are evaluated w.r.t. application domain: (i) speech activity detection, (ii) speaker segmentation and speaker role classification, as well as (iii) ASR. To our best knowledge, HAAWAII framework offers the best performing speech technologies for ATM, reaching high recognition accuracy (i.e., error-correction done by exploiting additional contextual data), robustness (i.e., models developed using large training corpora) and support for rapid domain transfer (i.e., to new ATM sector with minimum investment). Two scenarios provided by ANSPs were used for testing, achieving callsign detection accuracy of about 96% and 95% for NATS and ISAVIA, respectively.

Item URL in elib:https://elib.dlr.de/199970/
Document Type:Conference or Workshop Item (Speech)
Title:Automatic Speech Analysis Framework for ATC Communication in HAAWAII
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Nigmatulina, IuliiaIdiap, University of ZurichUNSPECIFIEDUNSPECIFIED
Helmke, HartmutUNSPECIFIEDhttps://orcid.org/0000-0002-1939-0200UNSPECIFIED
Ohneiser, OliverUNSPECIFIEDhttps://orcid.org/0000-0002-5411-691XUNSPECIFIED
Kleinert, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0002-0782-4147UNSPECIFIED
Date:28 November 2023
Journal or Publication Title:13th SESAR Innovation Days
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In ISI Web of Science:No
Keywords:HAAWAII project, Speech activity detection, Speaker segmentation, Speaker role classification, Automatic Speech Recognition
Event Title:13th SESAR Innovation Days
Event Location:Sevilla, Spanien
Event Type:international Conference
Event Start Date:27 November 2023
Event End Date:30 November 2023
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Air Transportation and Impact
DLR - Research area:Aeronautics
DLR - Program:L AI - Air Transportation and Impact
DLR - Research theme (Project):L - Integrated Flight Guidance
Location: Braunschweig
Institutes and Institutions:Institute of Flight Guidance > Controller Assistance
Deposited By: Ohneiser, Oliver
Deposited On:29 Nov 2023 10:47
Last Modified:24 Apr 2024 21:00

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