Automatic Speech Recognition and Spoken Language Understanding of Maritime Radio Communications: A case study with Singapore data
Phuong Dat, Jayakrishnan Melur Madhathil, Tran Huy Dat
Abstract
Speech communication has been a major part of maritime transportation, particularly in Vessel Traffic Management (VTM). Automatic Speech Recognition (ASR) and Spoken Language Understanding (SLU) of VTM communications provide shipping traffic information in digital forms which improves the productivity and efficiency of VTM operations. The task is, however, challenging due to specific conditions including low-quality audio signals from maritime radio communication channels, non-native accents from ship masters and constrained maritime spoken lingo which is different from natural spoken languages. This paper reports a pilot development of ASR and SLU on Singapore maritime radio communication data. We provide an overview of the dataset and block diagram processing of ASRU and SLU, respectively. This paper presents several contributions designed to improve the ASR and the SLU systems by releasing a dataset for ASR and SLU task in maritime domain. Firstly, we introduce an ASR dataset in the maritime domain which has the purpose of promoting research in the demanding field of maritime. Secondly, we provide experimental results on the efficacy of the different state-of-the-art ASR systems on the maritime dataset. Finally, we evaluate numerous SLU models with maritime dataset for SLU task and also provide some ideas to improve the capabilities of this system for use in the maritime domain in the future.
BibTeX
@inproceedings{icassp2025_automaticspeechr,
title = {Automatic Speech Recognition and Spoken Language Understanding of Maritime Radio Communications: A case study with Singapore data},
author = {Phuong Dat and Jayakrishnan Melur Madhathil and Tran Huy Dat},
booktitle = {ICASSP 2025},
year = {2025}
}