Adaptation and Optimization of Automatic Speech Recognition (ASR) for the Maritime Domain in the Field of VHF Communication
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Abstract (arXiv)
This paper introduces a multilingual automatic speech recognizer (ASR) for maritime radio communi-cation that automatically converts received VHF radio signals into text. The challenges of maritime radio communication are described at first, and the deep learning architecture of marFM consisting of audio processing techniques and machine learning algorithms is presented. Subsequently, maritime radio data of interest is analyzed and then used to evaluate the transcription performance of our ASR model for various maritime radio data.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 9 of its 29 references that are in the map. First posted on arXiv 1 Jun 2023.
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9 of 29 references.
References · 9
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- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,160
- 2022Robust Speech Recognition via Large-Scale Weak Supervisioncited by 3,972
- 2019Unsupervised Cross-lingual Representation Learning at Scalecited by 3,907
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- 2021Recent Advances in End-to-End Automatic Speech Recognitioncited by 138
- 2019Unsupervised speech representation learning using WaveNet autoencoderscited by 90
- 2022A Survey of Multilingual Models for Automatic Speech Recognitioncited by 21
- 2020Multiresolution and Multimodal Speech Recognition with Transformerscited by 10
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Citing papers per year: 2026: 1.
1 paper.