Ending the Blind Flight: Analyzing the Impact of Acoustic and Lexical Factors on WAV2VEC 2.0 in Air-Traffic Control
Author:
Affiliation:
1. Saarland University,Saarland Informatics Campus,Germany
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10388490/10389614/10389646.pdf?arnumber=10389646
Reference18 articles.
1. Automatic Speech Recognition Benchmark for Air-Traffic Communications
2. The ATCOSIM corpus of non-prompted clean air traffic control speech;Hofbauer
3. ATCO2 corpus: A Large-Scale Dataset for Research on Automatic Speech Recognition and Natural Language Understanding of Air Traffic Control Communications;Zuluaga-Gomez,2022
4. Ontology for Transcription of ATC Speech Commands of SESAR 2020 Solution PJ.16-04
5. How Does Pre-Trained Wav2Vec 2.0 Perform on Domain-Shifted Asr? an Extensive Benchmark on Air Traffic Control Communications
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