Minimum effort adaptation of automatic speech recognition system in air traffic management

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0201384" target="_blank" >RIV/00216305:26230/26:0201384 - isvavai.cz</a>

Result on the web

<a href="https://journals.open.tudelft.nl/ejtir/article/view/7531" target="_blank" >https://journals.open.tudelft.nl/ejtir/article/view/7531</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.59490/ejtir.2024.24.4.7531" target="_blank" >10.59490/ejtir.2024.24.4.7531</a>

Result language

angličtina

Original language name

Minimum effort adaptation of automatic speech recognition system in air traffic management

Original language description

Advancements in Automatic Speech Recognition (ASR) technology is exemplified by ubiquitous voice assistants such as Siri and Alexa. Researchers have been exploring the application of ASR for Air Traffic Management (ATM) systems. Initial prototypes utilized ASR to pre-fill aircraft radar labels and achieved a technological readiness level before industrialization (TRL6). However, accurately recognizing infrequently used but highly informative domain-specific vocabulary is still an issue. This includes waypoint names specific to each airspace region and unique airline designators, e.g., "DEXON" or "POBEDA". Traditionally, open-source ASR toolkits or large pre-trained models require substantial domain-specific transcribed speech data to adapt to specialized vocabularies. However, typically, a "universal" ASR engine capable of reliably recognizing a core dictionary of several hundreds of frequently used words suffices for ATM applications. The challenge lies in dynamically integrating the additional region-specific words used less frequently. These uncommon words are crucial for maintaining clear communication within the ATM environment. This paper proposes a novel approach that facilitates the dynamic integration of these new and specific word entities into the existing universal ASR system. This paves the way for "plug-and-play" customization with minimal expert intervention and eliminates the need for extensive fine-tuning of the universal ASR model. The proposed approach demonstrably improves the accuracy of these region-specific words by a factor of approximate to 7 (from 10% F1-score to 70%) for all rare words and approximate to 5 (from 13% F1-score to 64%) for waypoints.

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

50703 - Transport planning and social aspects of transport (transport engineering to be 2.1)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

European Journal of Transport and Infrastructure Research

ISSN

1567-7133

e-ISSN

1567-7141

Volume of the periodical

24

Issue of the periodical within the volume

4

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

21

Pages from-to

133-153

UT code for WoS article

001447236400001

EID of the result in the Scopus database

2-s2.0-85215400025